[Hiring] Staff Machine Learning Engineer @Babylist

Role Description

As a Staff Machine Learning Engineer at Babylist, you own personalization and decide where it goes. Millions of families depend on what we build. 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.

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
• The community around them

Personalization runs across all of it — the homepage feed, what we recommend next, search — plus the platform underneath and the AI we already ship 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 have a say in which bets we make, and you pick what you take on next.

A Staff MLE here sets direction. You own personalization as a domain:

• The models behind the homepage feed
• Add-next recommendations
• Search personalization
• The foundational representations several teams build on

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.
• 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.
• 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.
• Resolving one customer across registry, shop and health, plus the friends and family buying for them.
• 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.

Qualifications

• Experience shipping production ML for several years.
• Ability to pick up an ambiguous problem and start moving before the full picture is provided.
• Experience building recommender systems or personalization that reached real users at scale.
• Deep knowledge of the Python ML ecosystem (pandas, scikit-learn, XGBoost, PyTorch).
• Fluency across the whole lifecycle, from orchestration to monitoring.
• Ability to build custom representations from raw data instead of using off-the-shelf embeddings.

Requirements

• Measure yourself by impact: a customer outcome, or a model a dozen surfaces come to depend on.
• Go zero-to-one: define the problem space, architect from scratch and own it end to end.
• Curiosity: 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.