Staff Machine Learning Engineer

• 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
• 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
• 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)

Benefits

• HAPPIER WEEKENDS We work to live, not live to work. That means we generally don’t work nights or weekends, and we offer 10 paid holidays throughout the year, in addition to your paid time off.
• PAID TIME OFF THAT WORKS FOR YOU Life is unpredictable. Use your time off for what you need, whether that’s a vacation, family event or a day to unplug.
• PARENTAL LEAVE We offer a flexible, 12-week paid parental leave policy.
• FLEXIBLE BENEFITS Choose from benefits that support your working style, whether that’s remote, in the office or something in between.
• HEALTH & WELLNESS Wellness benefits include 100% paid coverage for medical, vision and dental for full-time employees.
• BABYLIST STORE DISCOUNT All full-time employees receive a generous Babylist Store discount.

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 onYou'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 journeyAn ability to take an ambiguous problem and start moving before anyone hands you the full pictureExpertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity biasA 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 learningsFluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backendAn 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 revenueAI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what’s coming next