Staff Machine Learning Engineer

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: Take a fuzzy business problem from the first sketch through to a production model, and stay on the hook for whether it actually helped customers. Build custom embeddings from raw data — domain-specific representations that go well beyond off-the-shelf image and text models — and own them as several surfaces adopt them. Make the modeling and architecture calls that span teams and the ones that are expensive to reverse. Own the full lifecycle: orchestration, deployment, monitoring and the retraining loop that keeps a model honest in production. Set the standard for how personalization builds with AI. Decide what good looks like, build the evals that catch a model that's confidently wrong before it ships. Partner with product, design and data as a peer, shaping what's worth building from the start. Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones. A few problems people at this level are working on right now: Building the foundational embeddings that let every surface — feed, recommendations, search — personalize from one shared representation instead of each team rebuilding it. Resolving one customer across registry, shop and health, plus the friends and family buying for them, so recommendations work everywhere. 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. Who You Are You've shipped production ML 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. You've built recommender systems or personalization that reached real users at scale, and you can point to what moved because of it. You're deep in the Python ML ecosystem (pandas, scikit-learn, XGBoost, PyTorch) and fluent across the whole lifecycle, from orchestration to monitoring, not just training. The thing that sets you apart: you build custom representations from raw data instead of reaching for the off-the-shelf embedding. A few things that tend to be true of people who thrive here: You measure yourself by impact: a customer outcome, or a model a dozen surfaces come to depend on. You go zero-to-one: define the problem space, architect from scratch and own it end to end. You're curious: you spot problems before they're filed and push your own ideas until they ship. 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 an RRSP 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