Staff Machine Learning Engineer

📋 Job Overview

As a Staff Machine Learning Engineer at Babylist, you own personalization and decide where it goes. Millions of families depend on what we 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're still in the code for the genuinely hard problems — the embeddings, the ranking, the systems that don't exist yet. Agents handle the volume. You spend your time on the parts that need a person.

🏢 About 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.

🎯 The Role

You'll own a big piece of how personalization works, 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.

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• 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.

🎁 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

🛂 Visa & Eligibility

Official outreach only ever comes from an @babylist.com address