Staff Machine Learning Engineer
Babylist · United States
About this role
**Staff Machine Learning Engineer — Babylist** ## What the Role Is As a **Staff Machine Learning Engineer** at Babylist, you own personalization and decide where it goes. Millions of families depend on what you build. Agents write most of the code now—so the hard part is yours: **what to model, how it should work, and whether what shipped actually helped**. You’ll stay in the code for genuinely hard problems like **embeddings, ranking, and systems that don’t exist yet**. Babylist is building well beyond the registry, including the financial side of raising a kid, maternal health, education for new parents, and the community around them. Personalization runs across it all—**homepage feed, next recommendations, search**, plus the platform underneath and the AI already shipped to families. You’ll own a big piece of how personalization works, but you won’t be boxed into it. The roadmap is open—you’ll help choose which bets we make and what you take on next. ## What You’ll Own A Staff MLE sets direction and owns personalization as a domain: - **Models behind the homepage feed, add-next recommendations, and search personalization** - **Foundational representations** multiple teams build on - Define where personalization goes over the next **1–2 years**, sequence the bets, and make the technical + product calls along the way In practice, you: - Take a fuzzy business problem from first sketch to **production model**, and stay accountable for whether it helped customers - Build **custom embeddings** from raw data (domain-specific representations beyond off-the-shelf models) - Make modeling + architecture calls across teams, including expensive-to-reverse decisions - Own the full lifecycle: **orchestration, deployment, monitoring, and retraining loops** - Set the standard for how personalization builds with AI: define what “good” looks like and build **evals** to catch confident wrong answers before shipping - Partner with product, design, and data as a peer to shape what’s worth building from the start - Coach senior engineers through hard, ambiguous decisions ### Problems you may work on - Building foundational embeddings so every surface (feed, recommendations, search) personalizes from one shared representation - Resolving one customer across registry, shop, and health (including friends/family buying for them) so recommendations work everywhere - Deciding what the registry recommends to each family (ranking + model) and proving impact via live experiments ## Who You Are - You’ve shipped **production ML** for enough years to have strong opinions (and you hold them loosely) - You can take an ambiguous problem and start moving before you have the full picture - You’ve built recommender systems/personalization that reached real users at scale, and you can point to what moved - Deep in the Python ML ecosystem (**pandas, scikit-learn, XGBoost, PyTorch**) and fluent across the lifecycle (orchestration → monitoring), not just training - You build **custom representations from raw data** instead of relying on off-the-shelf embeddings You tend to thrive if you: - Measure yourself by impact (customer outcomes or models many surfaces depend on) - Go zero-to-one: define the problem space, architect from scratch, and own end-to-end - Are curious: spot problems early and push ideas until they ship ## Compensation Babylist posts real numbers. For a US-based Staff Engineer: - **Starting base salary range:** $233,500 – $290,700 - **Target annual bon
Listing freshness
CronJobs last confirmed this listing 1h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.