Staff Machine Learning Engineer

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.

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 and 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.

What You'll Own

A Staff MLE here sets direction. You own personalization as a domain: the models behind the homepage feed, add-next recommendations and search personalization, and the foundational representations several teams build on. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building.

If you stepped away, multiple teams would feel it. Your models show up on the surfaces millions of families use, and in the work other teams choose to build on top of them. You don't need direct reports to have that reach. It comes from what you build.

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