[{"data":1,"prerenderedAt":108},["ShallowReactive",2],{"$feeYEO3cznLh-SGD7XeFfWeRkUplokyzd3NJiMdyBdoc":3,"$f-IlPK8Uze5DQLIDAHjiqiVqy8eyBYvJwfo_7o7xEgVc":29},{"job":4},{"id":5,"applyToken":6,"slug":7,"title":8,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":13,"country":13,"remote":14,"employmentType":15,"department":17,"content_html":18,"content_text":19,"years":20,"createdAt":21,"updatedAtISO":22,"postedAtISO":23,"hasSalary":14,"salaryMin":24,"salaryMax":25,"currency":26,"schema":27},"0d52431e34fc216b97132c414acbc5a006ff67e080724ba8878cbfeb215b4c67","0e40ce80926d779d5c5d2fa7cb56eace6bf01c223b6a338a68fb09e361d57ee1","staff-machine-learning-engineer-at-babylist-9d20f39274","Staff Machine Learning Engineer","Babylist","https://cdn.jobboardwise.com/logo/babylist.com","We help expecting parents get exactly what they need for the arrival of their new baby.","Retail","United States",false,[16],"Full-time","Engineering","\u003Cp>\u003Cstrong>What The Role Is\u003C/strong>\u003C/p>\n\u003Cp>We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless.\u003C/p>\n\u003Cp>Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting.\u003C/p>\n\u003Cp>We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next.\u003C/p>\n\u003Cp>Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. \u003C/p>\n\u003Cp>If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape:\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Product recommendations across every surface.\u003C/strong> Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them.\u003C/li>\n\u003Cli>\u003Cstrong>Personalizing the Feed.\u003C/strong> Deciding which products, editorial content and social content each family sees, and in what order. \u003C/li>\n\u003Cli>\u003Cstrong>Personalizing search.\u003C/strong> Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations.\u003C/li>\n\u003Cli>\u003Cstrong>Making Addie (our AI registry assistant) better at recs.\u003C/strong> Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site.\u003C/li>\n\u003C/ul>\n\u003Cp>In practice, you will:\u003C/p>\n\u003Cul>\n\u003Cli>Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric.\u003C/li>\n\u003Cli>Make the modeling and architecture calls that span surfaces and are expensive to reverse.\u003C/li>\n\u003Cli>Design the evaluation, offline and online, that tells us a model is good before and after it ships.\u003C/li>\n\u003Cli>Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches.\u003C/li>\n\u003Cli>Partner with product, design and data to help shape what's worth building.\u003C/li>\n\u003Cli>Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey.\u003C/p>\n\u003Cp>You bring: \u003C/p>\n\u003Cul>\n\u003Cli>Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias.\u003C/li>\n\u003Cli>Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend.\u003C/li>\n\u003Cli>An ability to take an ambiguous problem and start moving before anyone hands you the full picture. \u003C/li>\n\u003Cli>A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings.\u003C/li>\n\u003Cli>An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue.\u003C/li>\n\u003Cli>AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next.\u003C/li>\n\u003C/ul>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Cp>\u003Cstrong>How We Build \u003Cbr>\u003C/strong>\u003C/p>\n\u003Cp>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\u003C/p>\n\u003Cp>The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A 401(k) match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address\u003C/li>\n\u003C/ul>","What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address",0,1790997583000,"2026-10-06 05:22:57","2026-10-05T12:24:32-04:00",233500,290700,"USD",{"jsonldValid":14,"jsonld":28},"",{"jobs":30},[31,42,56,68,79,91],{"id":32,"applyToken":33,"slug":34,"title":8,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":35,"country":35,"remote":14,"employmentType":36,"department":17,"content_html":37,"content_text":38,"years":20,"createdAt":21,"updatedAtISO":22,"postedAtISO":39,"hasSalary":14,"salaryMin":20,"salaryMax":20,"currency":40,"schema":41},"919ec62c43587fcd814a7e53da01c6aaf5b70d3f56b8b874da57b05cbcfc9742","1877c7b3285adf72f1cb98830e7c0a12cf6b131cf707a79abc64a4064fa1459d","staff-machine-learning-engineer-at-babylist-6bfb7d93cb","Canada",[16],"\u003Cp>\u003Cstrong>What The Role Is\u003C/strong>\u003C/p>\n\u003Cp>We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless.\u003C/p>\n\u003Cp>Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting.\u003C/p>\n\u003Cp>We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next.\u003C/p>\n\u003Cp>Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. \u003C/p>\n\u003Cp>If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape:\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Product recommendations across every surface.\u003C/strong> Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them.\u003C/li>\n\u003Cli>\u003Cstrong>Personalizing the Feed.\u003C/strong> Deciding which products, editorial content and social content each family sees, and in what order. \u003C/li>\n\u003Cli>\u003Cstrong>Personalizing search.\u003C/strong> Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations.\u003C/li>\n\u003Cli>\u003Cstrong>Making Addie (our AI registry assistant) better at recs.\u003C/strong> Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site.\u003C/li>\n\u003C/ul>\n\u003Cp>In practice, you will:\u003C/p>\n\u003Cul>\n\u003Cli>Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric.\u003C/li>\n\u003Cli>Make the modeling and architecture calls that span surfaces and are expensive to reverse.\u003C/li>\n\u003Cli>Design the evaluation, offline and online, that tells us a model is good before and after it ships.\u003C/li>\n\u003Cli>Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches.\u003C/li>\n\u003Cli>Partner with product, design and data to help shape what's worth building.\u003C/li>\n\u003Cli>Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey.\u003C/p>\n\u003Cp>You bring: \u003C/p>\n\u003Cul>\n\u003Cli>Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias.\u003C/li>\n\u003Cli>Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend.\u003C/li>\n\u003Cli>An ability to take an ambiguous problem and start moving before anyone hands you the full picture. \u003C/li>\n\u003Cli>A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings.\u003C/li>\n\u003Cli>An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue.\u003C/li>\n\u003Cli>AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next.\u003C/li>\n\u003C/ul>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Cp>\u003Cstrong>How We Build \u003Cbr>\u003C/strong>\u003C/p>\n\u003Cp>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\u003C/p>\n\u003Cp>The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A 401(k) match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address\u003C/li>\n\u003C/ul>","What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address","2026-10-05T12:30:04-04:00","CAD",{"jsonldValid":14,"jsonld":28},{"id":43,"applyToken":44,"slug":45,"title":46,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":35,"country":35,"remote":14,"employmentType":47,"department":17,"content_html":48,"content_text":49,"years":20,"createdAt":50,"updatedAtISO":51,"postedAtISO":52,"hasSalary":14,"salaryMin":53,"salaryMax":54,"currency":40,"schema":55},"b084f0e88ced4840d1625afcc71b16f6a2cbf2e33e531eaca0a023472be0cffa","a606d9fe1676e5b774a8e8bac232672fa9fc19b6c2b87db4514b575f622bfd0c","senior-software-engineer-at-babylist-e753555faf","Senior Software Engineer",[16],"\u003Ch4>What The Role Is\u003C/h4>\n\u003Cp>As a\u003Cstrong> Senior Engineer\u003C/strong> at Babylist, you take on hard problems and own them end-to-end. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person.\u003C/p>\n\u003Cp>We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll start on a team, but you won't be boxed into it. The roadmap is open, your scope grows as you do and you have a say in what you take on next. Senior here means owning whole problems; Staff is the next step up, setting direction across a domain.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>A Senior Engineer here owns problems, not just features. You're handed an outcome, you decide what to build, ship it and own whether it worked. You act on incomplete information and change course fast when the evidence says you were wrong.\u003C/p>\n\u003Cp>What you do depends on what the team needs:\u003C/p>\n\u003Cul>\n\u003Cli>Frame the problem with your PM as a peer, run the experiment and bring customer evidence into what gets built.\u003C/li>\n\u003Cli>Harden a critical path, kill a class of incidents or design the abstraction your team builds on.\u003C/li>\n\u003Cli>Be the reviewer people want on the risky changes.\u003C/li>\n\u003Cli>Manage up with crisp choices: raise the risks early and protect the right tradeoffs.\u003C/li>\n\u003Cli>Share what you're learning about AI openly, so the whole team gets sharper.\u003C/li>\n\u003C/ul>\n\u003Cp>A few problems people at this level are working on right now:\u003C/p>\n\u003Cul>\n\u003Cli>Deciding what the registry recommends to each family, from the ranking to the model behind it, and proving in a live experiment that it actually helps.\u003C/li>\n\u003Cli>Building the evals system that catches our AI support agent making things up before a customer does.\u003C/li>\n\u003Cli>Making a child's info one source of truth instead of five disconnected flows, so parents set it up once and new products like Early Investor build on it.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You've been shipping production systems for years, and you've got the judgment to show for it. You can pick up an ambiguous problem and start moving before anyone hands you the full picture.\u003C/p>\n\u003Cp>The strongest Senior engineers here are generalists, and usually the most prolific builders on their team. You go wherever the problem leads: a backend service one week, a stubborn frontend bug the next, the data pipeline after that. When it's hard you dig in, and you learn unfamiliar parts of the stack fast. Range is the Senior signature.\u003C/p>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a Canada-based Senior Engineer, the starting base salary range is $255,900 to $318,600 CAD, plus a target annual bonus of 15 percent of base. That's total target cash of roughly $294,285 to $366,390 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Ch4>How We Build\u003C/h4>\n\u003Cp>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks, start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A RRSP match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address.\u003C/li>\n\u003C/ul>","What The Role Is\nAs a Senior Engineer at Babylist, you take on hard problems and own them end-to-end. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person.\nWe're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll start on a team, but you won't be boxed into it. The roadmap is open, your scope grows as you do and you have a say in what you take on next. Senior here means owning whole problems; Staff is the next step up, setting direction across a domain.\nWhat You'll Own\nA Senior Engineer here owns problems, not just features. You're handed an outcome, you decide what to build, ship it and own whether it worked. You act on incomplete information and change course fast when the evidence says you were wrong.\nWhat you do depends on what the team needs:\n\nFrame the problem with your PM as a peer, run the experiment and bring customer evidence into what gets built.\nHarden a critical path, kill a class of incidents or design the abstraction your team builds on.\nBe the reviewer people want on the risky changes.\nManage up with crisp choices: raise the risks early and protect the right tradeoffs.\nShare what you're learning about AI openly, so the whole team gets sharper.\n\nA few problems people at this level are working on right now:\n\nDeciding what the registry recommends to each family, from the ranking to the model behind it, and proving in a live experiment that it actually helps.\nBuilding the evals system that catches our AI support agent making things up before a customer does.\nMaking a child's info one source of truth instead of five disconnected flows, so parents set it up once and new products like Early Investor build on it.\n\nWho You Are\nYou've been shipping production systems for years, and you've got the judgment to show for it. You can pick up an ambiguous problem and start moving before anyone hands you the full picture.\nThe strongest Senior engineers here are generalists, and usually the most prolific builders on their team. You go wherever the problem leads: a backend service one week, a stubborn frontend bug the next, the data pipeline after that. When it's hard you dig in, and you learn unfamiliar parts of the stack fast. Range is the Senior signature.\nCompensation\nWe post real numbers. For a Canada-based Senior Engineer, the starting base salary range is $255,900 to $318,600 CAD, plus a target annual bonus of 15 percent of base. That's total target cash of roughly $294,285 to $366,390 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\nHow We Build\nAI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\nThe Stack\n\nCore app: Rails, Packwerk, React, TypeScript, Sidekiq\nMobile: iOS (Swift), Android (Kotlin)\nData, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\nMachine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\nAI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\nInfra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\nKey integrations: Shopify (payments), Iterable (CRM)\n\nWhy Babylist\nAn engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\nTen million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\nHow We Work\nRemote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\nHow We Hire\nThree rounds, usually two to three weeks, start to finish.\n\nRecruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote.\nTechnical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\nFinal round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\n\nIf your timeline is tight, tell us and we'll move faster.\nBenefits\n\nCompany-paid medical and fully covered dental and vision\nA RRSP match\nGenerous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\nWinter Wonder Week, a paid company-wide week off at the end of the year\nA remote-work stipend\nMental-health and wellness support\n\nA Few Things To Know\n\nWe record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\nWe expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\nIf you have a family member or close relationship with a Babylist employee, let your recruiter know.\nOfficial outreach only ever comes from an @babylist.com address.",1786764934000,"2026-09-08 05:25:14","2026-08-14T17:53:48.000Z",255900,318600,{"jsonldValid":14,"jsonld":28},{"id":57,"applyToken":58,"slug":59,"title":60,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":35,"country":35,"remote":14,"employmentType":61,"department":17,"content_html":62,"content_text":63,"years":20,"createdAt":50,"updatedAtISO":51,"postedAtISO":64,"hasSalary":14,"salaryMin":65,"salaryMax":66,"currency":40,"schema":67},"9dd136601945afe1253f208e1a8fb2e15502c39f8ec93301fbb99a756598034a","8e0dd8f89907c39c2a316174e6886c0631853233ff2f5e9e811881b0cd70e2a2","staff-software-engineer-at-babylist-1336192f7f","Staff Software Engineer",[16],"\u003Cp>\u003Cstrong>What The Role Is\u003C/strong>\u003C/p>\n\u003Cp>As a \u003Cstrong>Staff Engineer \u003C/strong>at Babylist, you own some of our hardest domains and decide where they go. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families.\u003C/p>\n\u003Cp>You'll own a big piece of the product, but you won't be boxed into it. The roadmap is open. You have a say in which bets we make, and you pick what you take on next.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>A Staff Engineer here sets direction. You own a domain: a product surface, a platform area or a capability that cuts across several teams. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building. If you stepped away, multiple teams would feel it. Your influence shows up in the systems and the work other teams choose to build on. You don't need direct reports to have that reach. It comes from what you build.\u003C/p>\n\u003Cp>In practice, you:\u003C/p>\n\u003Cul>\n\u003Cli>Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers.\u003C/li>\n\u003Cli>Build the platform or tooling other teams adopt by choice, and own it as it scales.\u003C/li>\n\u003Cli>Make the architecture calls that span teams and the ones that are expensive to reverse.\u003C/li>\n\u003Cli>Set the standard for how your domain builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships.\u003C/li>\n\u003Cli>Partner with product, design and data as a peer, shaping what's worth building from the start.\u003C/li>\n\u003Cli>Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones.\u003C/li>\n\u003C/ul>\n\u003Cp>A few problems people at this level are working on right now:\u003C/p>\n\u003Cul>\n\u003Cli>Resolving one customer across registry, shop and health, plus the friends and family buying for them, so personalization works everywhere without each team rebuilding it.\u003C/li>\n\u003Cli>Designing the knowledge system our coding agents reliably load, and working out how much context actually helps before it starts to hurt.\u003C/li>\n\u003Cli>Building Early Investor, our new family finance product, from scratch, on a deadline that can't move.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You've shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You've already changed how a team builds with AI, and the new way stuck. Most Staff engineers lean one of two ways, and both do well here. Some point their depth at the systems everyone runs on: the platforms, the reliability and security bar, the harnesses that make AI produce good code. Others point it at the customer: framing the problem with PMs as peers, owning a journey end to end, deciding what to build and learning whether it worked. You don't have to be both. You do have to be excellent at one and fluent in the other.\u003C/p>\n\u003Cp>A few things that tend to be true of people who thrive here:\u003C/p>\n\u003Cul>\n\u003Cli>You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on.\u003C/li>\n\u003Cli>You're curious: you spot problems before they're filed and push your own ideas until they ship.\u003C/li>\n\u003C/ul>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is $299,300 to $372,600 CAD, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $359,160 to $447,120 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Cp>\u003Cstrong>How We Build \u003Cbr>\u003Cbr>\u003C/strong>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\u003C/p>\n\u003Cp>The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A RRSP match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address\u003C/li>\n\u003C/ul>","What The Role Is\nAs a Staff Engineer at Babylist, you own some of our hardest domains and decide where they go. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families.\nYou'll own a big piece of the product, but you won't be boxed into it. The roadmap is open. You have a say in which bets we make, and you pick what you take on next.\nWhat You'll Own\nA Staff Engineer here sets direction. You own a domain: a product surface, a platform area or a capability that cuts across several teams. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building. If you stepped away, multiple teams would feel it. Your influence shows up in the systems and the work other teams choose to build on. You don't need direct reports to have that reach. It comes from what you build.\nIn practice, you:\n\nTake a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers.\nBuild the platform or tooling other teams adopt by choice, and own it as it scales.\nMake the architecture calls that span teams and the ones that are expensive to reverse.\nSet the standard for how your domain builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships.\nPartner with product, design and data as a peer, shaping what's worth building from the start.\nCoach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones.\n\nA few problems people at this level are working on right now:\n\nResolving one customer across registry, shop and health, plus the friends and family buying for them, so personalization works everywhere without each team rebuilding it.\nDesigning the knowledge system our coding agents reliably load, and working out how much context actually helps before it starts to hurt.\nBuilding Early Investor, our new family finance product, from scratch, on a deadline that can't move.\n\nWho You Are\nYou've shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You've already changed how a team builds with AI, and the new way stuck. Most Staff engineers lean one of two ways, and both do well here. Some point their depth at the systems everyone runs on: the platforms, the reliability and security bar, the harnesses that make AI produce good code. Others point it at the customer: framing the problem with PMs as peers, owning a journey end to end, deciding what to build and learning whether it worked. You don't have to be both. You do have to be excellent at one and fluent in the other.\nA few things that tend to be true of people who thrive here:\n\nYou measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on.\nYou're curious: you spot problems before they're filed and push your own ideas until they ship.\n\nCompensation\nWe post real numbers. For a Canada-based Staff Engineer, the starting base salary range is $299,300 to $372,600 CAD, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $359,160 to $447,120 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\nHow We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\nThe architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\nThe Stack\n\nCore app: Rails, Packwerk, React, TypeScript, Sidekiq\nMobile: iOS (Swift), Android (Kotlin)\nData, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\nMachine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\nAI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\nInfra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\nKey integrations: Shopify (payments), Iterable (CRM)\n\nWhy Babylist\nAn engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\nTen million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\nHow We Work\nRemote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\nHow We Hire\nThree rounds, usually two to three weeks start to finish.\n\nRecruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote.\nTechnical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\nFinal round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\n\nIf your timeline is tight, tell us and we'll move faster.\nBenefits\n\nCompany-paid medical and fully covered dental and vision\nA RRSP match\nGenerous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\nWinter Wonder Week, a paid company-wide week off at the end of the year\nA remote-work stipend\nMental-health and wellness support\n\nA Few Things To Know\n\nWe record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\nWe expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\nIf you have a family member or close relationship with a Babylist employee, let your recruiter know.\nOfficial outreach only ever comes from an @babylist.com address","2026-08-14T17:53:46.000Z",299300,372600,{"jsonldValid":14,"jsonld":28},{"id":69,"applyToken":70,"slug":71,"title":60,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":13,"country":13,"remote":14,"employmentType":72,"department":17,"content_html":73,"content_text":74,"years":20,"createdAt":75,"updatedAtISO":76,"postedAtISO":77,"hasSalary":14,"salaryMin":24,"salaryMax":25,"currency":26,"schema":78},"ef6b873b02a1473a10c74905c632df794404be54d79ae6fe147d97347d268b13","c40265a3a34e009a8703c6e4a836c27b28421c73925255de0fb87e5c9f96eab5","staff-software-engineer-at-babylist-145651fc45",[16],"\u003Cp>\u003Cstrong>What The Role Is\u003C/strong>\u003C/p>\n\u003Cp>As a \u003Cstrong>Staff Engineer \u003C/strong>at Babylist, you own some of our hardest domains and decide where they go. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families.\u003C/p>\n\u003Cp>You'll own a big piece of the product, but you won't be boxed into it. The roadmap is open. You have a say in which bets we make, and you pick what you take on next.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>A Staff Engineer here sets direction. You own a domain: a product surface, a platform area or a capability that cuts across several teams. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building. If you stepped away, multiple teams would feel it. Your influence shows up in the systems and the work other teams choose to build on. You don't need direct reports to have that reach. It comes from what you build.\u003C/p>\n\u003Cp>In practice, you:\u003C/p>\n\u003Cul>\n\u003Cli>Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers.\u003C/li>\n\u003Cli>Build the platform or tooling other teams adopt by choice, and own it as it scales.\u003C/li>\n\u003Cli>Make the architecture calls that span teams and the ones that are expensive to reverse.\u003C/li>\n\u003Cli>Set the standard for how your domain builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships.\u003C/li>\n\u003Cli>Partner with product, design and data as a peer, shaping what's worth building from the start.\u003C/li>\n\u003Cli>Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones.\u003C/li>\n\u003C/ul>\n\u003Cp>A few problems people at this level are working on right now:\u003C/p>\n\u003Cul>\n\u003Cli>Resolving one customer across registry, shop and health, plus the friends and family buying for them, so personalization works everywhere without each team rebuilding it.\u003C/li>\n\u003Cli>Designing the knowledge system our coding agents reliably load, and working out how much context actually helps before it starts to hurt.\u003C/li>\n\u003Cli>Building Early Investor, our new family finance product, from scratch, on a deadline that can't move.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You've shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You've already changed how a team builds with AI, and the new way stuck. Most Staff engineers lean one of two ways, and both do well here. Some point their depth at the systems everyone runs on: the platforms, the reliability and security bar, the harnesses that make AI produce good code. Others point it at the customer: framing the problem with PMs as peers, owning a journey end to end, deciding what to build and learning whether it worked. You don't have to be both. You do have to be excellent at one and fluent in the other.\u003C/p>\n\u003Cp>A few things that tend to be true of people who thrive here:\u003C/p>\n\u003Cul>\n\u003Cli>You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on.\u003C/li>\n\u003Cli>You're curious: you spot problems before they're filed and push your own ideas until they ship.\u003C/li>\n\u003C/ul>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Cp>\u003Cstrong>How We Build \u003Cbr>\u003Cbr>\u003C/strong>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\u003C/p>\n\u003Cp>The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A 401(k) match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address\u003C/li>\n\u003C/ul>","What The Role Is\nAs a Staff Engineer at Babylist, you own some of our hardest domains and decide where they go. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families.\nYou'll own a big piece of the product, but you won't be boxed into it. The roadmap is open. You have a say in which bets we make, and you pick what you take on next.\nWhat You'll Own\nA Staff Engineer here sets direction. You own a domain: a product surface, a platform area or a capability that cuts across several teams. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building. If you stepped away, multiple teams would feel it. Your influence shows up in the systems and the work other teams choose to build on. You don't need direct reports to have that reach. It comes from what you build.\nIn practice, you:\n\nTake a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers.\nBuild the platform or tooling other teams adopt by choice, and own it as it scales.\nMake the architecture calls that span teams and the ones that are expensive to reverse.\nSet the standard for how your domain builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships.\nPartner with product, design and data as a peer, shaping what's worth building from the start.\nCoach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones.\n\nA few problems people at this level are working on right now:\n\nResolving one customer across registry, shop and health, plus the friends and family buying for them, so personalization works everywhere without each team rebuilding it.\nDesigning the knowledge system our coding agents reliably load, and working out how much context actually helps before it starts to hurt.\nBuilding Early Investor, our new family finance product, from scratch, on a deadline that can't move.\n\nWho You Are\nYou've shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You've already changed how a team builds with AI, and the new way stuck. Most Staff engineers lean one of two ways, and both do well here. Some point their depth at the systems everyone runs on: the platforms, the reliability and security bar, the harnesses that make AI produce good code. Others point it at the customer: framing the problem with PMs as peers, owning a journey end to end, deciding what to build and learning whether it worked. You don't have to be both. You do have to be excellent at one and fluent in the other.\nA few things that tend to be true of people who thrive here:\n\nYou measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on.\nYou're curious: you spot problems before they're filed and push your own ideas until they ship.\n\nCompensation\nWe post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\nHow We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.\nThe architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\nThe Stack\n\nCore app: Rails, Packwerk, React, TypeScript, Sidekiq\nMobile: iOS (Swift), Android (Kotlin)\nData, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\nMachine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\nAI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\nInfra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\nKey integrations: Shopify (payments), Iterable (CRM)\n\nWhy Babylist\nAn engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\nTen million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\nHow We Work\nRemote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\nHow We Hire\nThree rounds, usually two to three weeks start to finish.\n\nRecruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote.\nTechnical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\nFinal round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\n\nIf your timeline is tight, tell us and we'll move faster.\nBenefits\n\nCompany-paid medical and fully covered dental and vision\nA 401(k) match\nGenerous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\nWinter Wonder Week, a paid company-wide week off at the end of the year\nA remote-work stipend\nMental-health and wellness support\n\nA Few Things To Know\n\nWe record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\nWe expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\nIf you have a family member or close relationship with a Babylist employee, let your recruiter know.\nOfficial outreach only ever comes from an @babylist.com address",1783272388000,"2026-09-08 19:31:52","2026-09-03T17:15:06-04:00",{"jsonldValid":14,"jsonld":28},{"id":80,"applyToken":81,"slug":82,"title":46,"companyname":9,"companylogo":10,"companyTagline":11,"companyIndustry":12,"city":13,"country":13,"remote":14,"employmentType":83,"department":17,"content_html":84,"content_text":85,"years":20,"createdAt":86,"updatedAtISO":87,"postedAtISO":77,"hasSalary":14,"salaryMin":88,"salaryMax":89,"currency":26,"schema":90},"27f5f85ab5d42a28df84851460a6bada7ccc088b59947846e51bb6adc99a75c2","d5bf2083f02a9028d6e496aa2fab066a6cb21d20fe685c35c458775c7eb0df86","senior-software-engineer-at-babylist-d3ea27fc3a",[16],"\u003Ch4>What The Role Is\u003C/h4>\n\u003Cp>As a\u003Cstrong> Senior Engineer\u003C/strong> at Babylist, you take on hard problems and own them end-to-end. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person.\u003C/p>\n\u003Cp>We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll start on a team, but you won't be boxed into it. The roadmap is open, your scope grows as you do and you have a say in what you take on next. Senior here means owning whole problems; Staff is the next step up, setting direction across a domain.\u003C/p>\n\u003Ch4>What You'll Own\u003C/h4>\n\u003Cp>A Senior Engineer here owns problems, not just features. You're handed an outcome, you decide what to build, ship it and own whether it worked. You act on incomplete information and change course fast when the evidence says you were wrong.\u003C/p>\n\u003Cp>What you do depends on what the team needs:\u003C/p>\n\u003Cul>\n\u003Cli>Frame the problem with your PM as a peer, run the experiment and bring customer evidence into what gets built.\u003C/li>\n\u003Cli>Harden a critical path, kill a class of incidents or design the abstraction your team builds on.\u003C/li>\n\u003Cli>Be the reviewer people want on the risky changes.\u003C/li>\n\u003Cli>Manage up with crisp choices: raise the risks early and protect the right tradeoffs.\u003C/li>\n\u003Cli>Share what you're learning about AI openly, so the whole team gets sharper.\u003C/li>\n\u003C/ul>\n\u003Cp>A few problems people at this level are working on right now:\u003C/p>\n\u003Cul>\n\u003Cli>Deciding what the registry recommends to each family, from the ranking to the model behind it, and proving in a live experiment that it actually helps.\u003C/li>\n\u003Cli>Building the evals system that catches our AI support agent making things up before a customer does.\u003C/li>\n\u003Cli>Making a child's info one source of truth instead of five disconnected flows, so parents set it up once and new products like Early Investor build on it.\u003C/li>\n\u003C/ul>\n\u003Ch4>Who You Are\u003C/h4>\n\u003Cp>You've been shipping production systems for years, and you've got the judgment to show for it. You can pick up an ambiguous problem and start moving before anyone hands you the full picture.\u003C/p>\n\u003Cp>The strongest Senior engineers here are generalists, and usually the most prolific builders on their team. You go wherever the problem leads: a backend service one week, a stubborn frontend bug the next, the data pipeline after that. When it's hard you dig in, and you learn unfamiliar parts of the stack fast. Range is the Senior signature.\u003C/p>\n\u003Ch4>Compensation\u003C/h4>\n\u003Cp>We post real numbers. For a US-based Senior Engineer, the starting base salary range is $202,200 to $251,700, plus a target annual bonus of 15 percent of base. That's total target cash of roughly $232,530 to $289,455. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\u003C/p>\n\u003Ch4>How We Build\u003C/h4>\n\u003Cp>AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\u003C/p>\n\u003Cp>\u003Cstrong>The Stack\u003C/strong>\u003C/p>\n\u003Cul>\n\u003Cli>\u003Cstrong>Core app:\u003C/strong> Rails, Packwerk, React, TypeScript, Sidekiq\u003C/li>\n\u003Cli>\u003Cstrong>Mobile:\u003C/strong> iOS (Swift), Android (Kotlin)\u003C/li>\n\u003Cli>\u003Cstrong>Data, search & events:\u003C/strong> MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\u003C/li>\n\u003Cli>\u003Cstrong>Machine learning:\u003C/strong> deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\u003C/li>\n\u003Cli>\u003Cstrong>AI & dev tooling:\u003C/strong> Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\u003C/li>\n\u003Cli>\u003Cstrong>Infra & ops:\u003C/strong> AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\u003C/li>\n\u003Cli>\u003Cstrong>Key integrations:\u003C/strong> Shopify (payments), Iterable (CRM)\u003C/li>\n\u003C/ul>\n\u003Ch4>Why Babylist\u003C/h4>\n\u003Cp>An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\u003C/p>\n\u003Cp>Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\u003C/p>\n\u003Ch4>How We Work\u003C/h4>\n\u003Cp>Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\u003C/p>\n\u003Ch4>How We Hire\u003C/h4>\n\u003Cp>Three rounds, usually two to three weeks, start to finish.\u003C/p>\n\u003Col>\n\u003Cli>\u003Cstrong>Recruiter conversation (30 minutes).\u003C/strong> Trade context: what you want next, what we're building and straight answers on comp, team and remote.\u003C/li>\n\u003Cli>\u003Cstrong>Technical screen (1 hour).\u003C/strong> One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\u003C/li>\n\u003Cli>\u003Cstrong>Final round (4 hours).\u003C/strong> Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\u003C/li>\n\u003C/ol>\n\u003Cp>If your timeline is tight, tell us and we'll move faster.\u003C/p>\n\u003Ch4>Benefits\u003C/h4>\n\u003Cul>\n\u003Cli>Company-paid medical and fully covered dental and vision\u003C/li>\n\u003Cli>A 401(k) match\u003C/li>\n\u003Cli>Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\u003C/li>\n\u003Cli>Winter Wonder Week, a paid company-wide week off at the end of the year\u003C/li>\n\u003Cli>A remote-work stipend\u003C/li>\n\u003Cli>Mental-health and wellness support\u003C/li>\n\u003C/ul>\n\u003Ch4>A Few Things To Know\u003C/h4>\n\u003Cul>\n\u003Cli>We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\u003C/li>\n\u003Cli>We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\u003C/li>\n\u003Cli>If you have a family member or close relationship with a Babylist employee, let your recruiter know.\u003C/li>\n\u003Cli>Official outreach only ever comes from an @babylist.com address.\u003C/li>\n\u003C/ul>","What The Role Is\nAs a Senior Engineer at Babylist, you take on hard problems and own them end-to-end. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person.\nWe're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll start on a team, but you won't be boxed into it. The roadmap is open, your scope grows as you do and you have a say in what you take on next. Senior here means owning whole problems; Staff is the next step up, setting direction across a domain.\nWhat You'll Own\nA Senior Engineer here owns problems, not just features. You're handed an outcome, you decide what to build, ship it and own whether it worked. You act on incomplete information and change course fast when the evidence says you were wrong.\nWhat you do depends on what the team needs:\n\nFrame the problem with your PM as a peer, run the experiment and bring customer evidence into what gets built.\nHarden a critical path, kill a class of incidents or design the abstraction your team builds on.\nBe the reviewer people want on the risky changes.\nManage up with crisp choices: raise the risks early and protect the right tradeoffs.\nShare what you're learning about AI openly, so the whole team gets sharper.\n\nA few problems people at this level are working on right now:\n\nDeciding what the registry recommends to each family, from the ranking to the model behind it, and proving in a live experiment that it actually helps.\nBuilding the evals system that catches our AI support agent making things up before a customer does.\nMaking a child's info one source of truth instead of five disconnected flows, so parents set it up once and new products like Early Investor build on it.\n\nWho You Are\nYou've been shipping production systems for years, and you've got the judgment to show for it. You can pick up an ambiguous problem and start moving before anyone hands you the full picture.\nThe strongest Senior engineers here are generalists, and usually the most prolific builders on their team. You go wherever the problem leads: a backend service one week, a stubborn frontend bug the next, the data pipeline after that. When it's hard you dig in, and you learn unfamiliar parts of the stack fast. Range is the Senior signature.\nCompensation\nWe post real numbers. For a US-based Senior Engineer, the starting base salary range is $202,200 to $251,700, plus a target annual bonus of 15 percent of base. That's total target cash of roughly $232,530 to $289,455. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.\nHow We Build\nAI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.\nThe Stack\n\nCore app: Rails, Packwerk, React, TypeScript, Sidekiq\nMobile: iOS (Swift), Android (Kotlin)\nData, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma\nMachine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow\nAI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim\nInfra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly\nKey integrations: Shopify (payments), Iterable (CRM)\n\nWhy Babylist\nAn engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.\nTen million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.\nHow We Work\nRemote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.\nHow We Hire\nThree rounds, usually two to three weeks, start to finish.\n\nRecruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote.\nTechnical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.\nFinal round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.\n\nIf your timeline is tight, tell us and we'll move faster.\nBenefits\n\nCompany-paid medical and fully covered dental and vision\nA 401(k) match\nGenerous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program\nWinter Wonder Week, a paid company-wide week off at the end of the year\nA remote-work stipend\nMental-health and wellness support\n\nA Few Things To Know\n\nWe record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.\nWe expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.\nIf you have a family member or close relationship with a Babylist employee, let your recruiter know.\nOfficial outreach only ever comes from an @babylist.com address.",1780457237000,"2026-09-08 19:31:48",202200,251700,{"jsonldValid":14,"jsonld":28},{"id":92,"applyToken":93,"slug":94,"title":95,"companyname":96,"companylogo":28,"city":97,"country":98,"remote":14,"employmentType":99,"department":100,"content_html":101,"content_text":102,"years":20,"createdAt":103,"updatedAtISO":104,"postedAtISO":105,"hasSalary":14,"salaryMin":20,"salaryMax":20,"currency":106,"schema":107},"c57647b72126c49531fd5b22c56d7ec8b571a1ff76cacb699a9cb0102b1b07c1","84ad3876ea9bafb161ad67193a2d1a8d77d1058eeab15072cd360cd0e8e04b09","digital-marketing-specialist-at-gartner-ef157bc38e","Digital Marketing Specialist","gartner","Gurgaon - Cyber Park","India",[16],"Other","\u003Cp>Global Sales Strategy & Operations (GSSO) is the team that helps shape Gartner's mission-critical sales priorities and works with sales leaders to drive tactical and analytical insights. As an associate on the GSSO team, you'll be at the forefront of the ongoing transformation of Gartner's sales force, which delivers approximately $4.9B in annual revenue and working to drive sustained double-digit growth. You will partner with business leaders across Gartner to support a global sales force comprised of more than 5,000 associates who sell to every major function, industry and market sector around the world.\u003C/p>\u003Cbr>\u003Cp>\u003Cb>\u003Cu>About the role:\u003C/u>\u003C/b>\u003C/p>\u003Cp>The role is a part of the Global Marketing team in GSSO. Within GSSO, the Global Marketing team designs digital and in-person marketing programs to help prospective customers understand how Gartner’s insights, advice, and tools can help them achieve the mission critical priorities that drive organizational performance. The team accelerates sales activity by attracting, engaging and converting prospects through the delivery of compelling experiences across the buyer journey.\u003C/p>\u003Cbr>\u003Cp>The Email Marketing Specialist will join the Digital Acquisition team and will work on organizing and managing email communications to clients and/or prospects. Specific duties include but are not limited to list management, email development and deployment using Eloqua 10, metric pulls and analysis, and testing of new approaches to email communication. This position will have the opportunity to develop business skills and acquire a broad understanding of Gartner with growth opportunities in digital marketing.\u003C/p>\u003Cbr>\u003Cp>\u003Cb>\u003Cu>What you will do:\u003C/u>\u003C/b>\u003C/p>\u003Cp>\u003Cb>Production\u003C/b>\u003C/p>\u003Cp>●  Manage deployment of inbound and outbound email communications\u003C/p>\u003Cp>●  Create HTML files for email deployment\u003C/p>\u003Cp>●  Set up, test, and deploy emails tied to specific lists\u003C/p>\u003Cp>●  Assist with distribution list creation and alignment\u003C/p>\u003Cp>●  Help maintain the communications calendar to ensure right balance of outreach and timely completion of material\u003C/p>\u003Cp>●  Contribute to various production tracking reports\u003C/p>\u003Cp>●  Collaborate with stakeholders and peers effectively to gather campaign production requirements and ensure appropriate audiences are reached effectively at the optimal times with the ideal message\u003C/p>\u003Cp>●  Conduct QA/QC as needed\u003C/p>\u003Cp>●  Help identify opportunities for business process improvement and implementation of new technologies\u003C/p>\u003Cbr>\u003Cp>\u003Cb>Analysis\u003C/b>\u003C/p>\u003Cp>●  Assist with analysis to determine email effectiveness\u003C/p>\u003Cp>●  Create reports and dashboards on email and web product performance. Identify opportunities for improvement based on performance\u003C/p>\u003Cp>●  Recommend changes to optimize programs based on analytical findings, business/industry best practices and current business needs\u003C/p>\u003Cbr>\u003Cp>\u003Cb>\u003Cu>What you will need:\u003C/u>\u003C/b>\u003C/p>\u003Cp>●  Bachelor’s degree in arts or sciences\u003C/p>\u003Cp>●  Industry experience of minimum 2 years\u003C/p>\u003Cp>●  Good Excel skills and ability to work with Formulas and formatting in Excel\u003C/p>\u003Cp>●  Experience of working with global stakeholders\u003C/p>\u003Cp>●  Experience of Eloqua or similar Marketing Automation tool\u003C/p>\u003Cp>●  Experience of HTML is preferred\u003C/p>\u003Cp>●  Excellent oral and written communication skills\u003C/p>\u003Cp>●  Strong attention to detail\u003C/p>\u003Cp>●  Exceptional client service ethic\u003C/p>\u003Cp>●  Strong work ethic and willingness to take ownership for wide-ranging responsibilities\u003C/p>\u003Cp>●  Superior problem-solving ability and ability to think “outside the box.”\u003C/p>\u003Cp>●  Ability to form and test hypotheses with available data and industry/corporate best practices\u003C/p>\u003Cp>●  Tolerance for ambiguity and self-drive to operate in an entrepreneurial setting\u003C/p>\u003Cp>●  Proven ability of managing tasks from beginning to end with strict adherence to deadlines\u003C/p>\u003Cbr>\u003Cp>\u003Cb>\u003Cu>What you will get\u003C/u>:\u003C/b>\u003C/p>\u003Cp>●  Competitive salary, generous paid time off policy, charity match program, Group Medical Insurance, Parental Leave, Employee Assistance Program (EAP) and more!\u003C/p>\u003Cp>●  Collaborative, team-oriented culture that embraces diversity\u003C/p>\u003Cp>●  Professional development and unlimited growth opportunities\u003C/p>\u003Cbr>\u003Cp>#LI-SJ1 #GSSO\u003C/p>\u003Cp>\u003Cb>Who are we? \u003C/b>\u003C/p>\u003Cp>At Gartner, Inc. (NYSE:IT), we guide the leaders who shape the world.\u003C/p>\u003Cp>Our mission relies on expert analysis and bold ideas to deliver actionable, objective business and technology insights, helping enterprise leaders and their teams succeed with their mission-critical priorities.\u003C/p>\u003Cp>Since our founding in 1979, we’ve grown to 20,000 associates globally who support over 13,000 client enterprises in ~90 countries and territories. We do important, interesting and substantive work that matters. That’s why we hire associates with the intellectual curiosity, energy and drive to want to make a difference. The bar is unapologetically high. So is the impact you can have here.\u003C/p>\u003Cp>\u003Cb>What makes Gartner a great place to work? \u003C/b>\u003C/p>\u003Cp>Our vast, virtually untapped market potential offers limitless opportunities – opportunities that may not even exist right now – for you to grow professionally and flourish personally. How far you go is driven by your passion and performance.\u003C/p>\u003Cp>We hire remarkable people who collaborate and win as a team. Together, our singular, unifying goal is to deliver results for our clients.\u003C/p>\u003Cp>Our teams are inclusive and composed of individuals from different geographies, cultures, religions, ethnicities, races, genders, sexual orientations, abilities and generations.\u003C/p>\u003Cp>We invest in great leaders who bring out the best in you and the company, enabling us to multiply our impact and results. This is why, year after year, we are recognized worldwide as a great place to work.\u003C/p>\u003Cp>\u003Cb>Gartner is the world authority on AI\u003C/b>\u003C/p>\u003Cp>At Gartner, you’ll join a company at the very center of the AI revolution. Gartner has proactive, objective guidance throughout clients’ AI journeys. We set the standard for how organizations leverage artificial intelligence to drive meaningful impact. You’ll have access to unmatched resources, expertise, and technology, and play a key role in helping Gartner and our clients innovate and grow as we leverage AI to transform business and technology landscapes.\u003C/p>\u003Cp>It’s an exciting time to be at Gartner, with limitless opportunities to make a real impact, grow your skills, and build a lasting, meaningful career in a field that’s reshaping the way we operate. If you’re passionate about AI and want to be part of a team that’s guiding the leaders who shape the world, Gartner is the place for you.\u003C/p>\u003Cp>\u003Cb>What do we offer? \u003C/b>\u003C/p>\u003Cp>Gartner offers world-class benefits, highly competitive compensation and disproportionate rewards for top performers.\u003C/p>\u003Cp>In our hybrid work environment, we provide the flexibility and support for you to thrive — working virtually when it's productive to do so and getting together with colleagues in a vibrant community that is purposeful, engaging and inspiring.\u003C/p>\u003Cp>Ready to grow your career with Gartner? Join us.\u003C/p>\u003Cp>\u003Cbr>The policy of Gartner is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to seek to advance the principles of equal employment opportunity.\u003C/p>\u003Cp>Gartner is committed to being an Equal Opportunity Employer and offers opportunities to all job seekers, including job seekers with disabilities. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by calling Human Resources at +1 (203) 964-0096 or by sending an email to ApplicantAccommodations@gartner.com.\u003C/p>Job Requisition ID:114444\u003Cp>By submitting your information and application, you confirm that you have read and agree to the country or regional recruitment notice linked below applicable to your place of residence.\u003C/p>\u003Cp>Gartner Applicant Privacy Link: https://jobs.gartner.com/applicant-privacy-policy\u003C/p>\u003Cp>\u003Cbr>\u003Cb>For efficient navigation through the application, please only use the back button within the application, not the back arrow within your browser.\u003C/b>\u003C/p>","Global Sales Strategy & Operations (GSSO) is the team that helps shape Gartner's mission-critical sales priorities and works with sales leaders to drive tactical and analytical insights. As an associate on the GSSO team, you'll be at the forefront of the ongoing transformation of Gartner's sales force, which delivers approximately $4.9B in annual revenue and working to drive sustained double-digit growth. You will partner with business leaders across Gartner to support a global sales force comprised of more than 5,000 associates who sell to every major function, industry and market sector around the world. About the role: The role is a part of the Global Marketing team in GSSO. Within GSSO, the Global Marketing team designs digital and in-person marketing programs to help prospective customers understand how Gartner’s insights, advice, and tools can help them achieve the mission critical priorities that drive organizational performance. The team accelerates sales activity by attracting, engaging and converting prospects through the delivery of compelling experiences across the buyer journey. The Email Marketing Specialist will join the Digital Acquisition team and will work on organizing and managing email communications to clients and/or prospects. Specific duties include but are not limited to list management, email development and deployment using Eloqua 10, metric pulls and analysis, and testing of new approaches to email communication. This position will have the opportunity to develop business skills and acquire a broad understanding of Gartner with growth opportunities in digital marketing. What you will do: Production ● Manage deployment of inbound and outbound email communications ● Create HTML files for email deployment ● Set up, test, and deploy emails tied to specific lists ● Assist with distribution list creation and alignment ● Help maintain the communications calendar to ensure right balance of outreach and timely completion of material ● Contribute to various production tracking reports ● Collaborate with stakeholders and peers effectively to gather campaign production requirements and ensure appropriate audiences are reached effectively at the optimal times with the ideal message ● Conduct QA/QC as needed ● Help identify opportunities for business process improvement and implementation of new technologies Analysis ● Assist with analysis to determine email effectiveness ● Create reports and dashboards on email and web product performance. Identify opportunities for improvement based on performance ● Recommend changes to optimize programs based on analytical findings, business/industry best practices and current business needs What you will need: ● Bachelor’s degree in arts or sciences ● Industry experience of minimum 2 years ● Good Excel skills and ability to work with Formulas and formatting in Excel ● Experience of working with global stakeholders ● Experience of Eloqua or similar Marketing Automation tool ● Experience of HTML is preferred ● Excellent oral and written communication skills ● Strong attention to detail ● Exceptional client service ethic ● Strong work ethic and willingness to take ownership for wide-ranging responsibilities ● Superior problem-solving ability and ability to think “outside the box.” ● Ability to form and test hypotheses with available data and industry/corporate best practices ● Tolerance for ambiguity and self-drive to operate in an entrepreneurial setting ● Proven ability of managing tasks from beginning to end with strict adherence to deadlines What you will get : ● Competitive salary, generous paid time off policy, charity match program, Group Medical Insurance, Parental Leave, Employee Assistance Program (EAP) and more! ● Collaborative, team-oriented culture that embraces diversity ● Professional development and unlimited growth opportunities #LI-SJ1 #GSSO Who are we? At Gartner, Inc. (NYSE:IT), we guide the leaders who shape the world. Our mission relies on expert analysis and bold ideas to deliver actionable, objective business and technology insights, helping enterprise leaders and their teams succeed with their mission-critical priorities. Since our founding in 1979, we’ve grown to 20,000 associates globally who support over 13,000 client enterprises in ~90 countries and territories. We do important, interesting and substantive work that matters. That’s why we hire associates with the intellectual curiosity, energy and drive to want to make a difference. The bar is unapologetically high. So is the impact you can have here. What makes Gartner a great place to work? Our vast, virtually untapped market potential offers limitless opportunities – opportunities that may not even exist right now – for you to grow professionally and flourish personally. How far you go is driven by your passion and performance. We hire remarkable people who collaborate and win as a team. Together, our singular, unifying goal is to deliver results for our clients. Our teams are inclusive and composed of individuals from different geographies, cultures, religions, ethnicities, races, genders, sexual orientations, abilities and generations. We invest in great leaders who bring out the best in you and the company, enabling us to multiply our impact and results. This is why, year after year, we are recognized worldwide as a great place to work. Gartner is the world authority on AI At Gartner, you’ll join a company at the very center of the AI revolution. Gartner has proactive, objective guidance throughout clients’ AI journeys. We set the standard for how organizations leverage artificial intelligence to drive meaningful impact. You’ll have access to unmatched resources, expertise, and technology, and play a key role in helping Gartner and our clients innovate and grow as we leverage AI to transform business and technology landscapes. It’s an exciting time to be at Gartner, with limitless opportunities to make a real impact, grow your skills, and build a lasting, meaningful career in a field that’s reshaping the way we operate. If you’re passionate about AI and want to be part of a team that’s guiding the leaders who shape the world, Gartner is the place for you. What do we offer? Gartner offers world-class benefits, highly competitive compensation and disproportionate rewards for top performers. In our hybrid work environment, we provide the flexibility and support for you to thrive — working virtually when it's productive to do so and getting together with colleagues in a vibrant community that is purposeful, engaging and inspiring. Ready to grow your career with Gartner? Join us. The policy of Gartner is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to seek to advance the principles of equal employment opportunity. Gartner is committed to being an Equal Opportunity Employer and offers opportunities to all job seekers, including job seekers with disabilities. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by calling Human Resources at +1 (203) 964-0096 or by sending an email to ApplicantAccommodations@gartner.com. Job Requisition ID:114444 By submitting your information and application, you confirm that you have read and agree to the country or regional recruitment notice linked below applicable to your place of residence. Gartner Applicant Privacy Link: https://jobs.gartner.com/applicant-privacy-policy For efficient navigation through the application, please only use the back button within the application, not the back arrow within your browser.",1791429812000,"2026-10-08 05:27:31","Posted Today","INR",{"jsonldValid":14,"jsonld":28},1791448972031]