As a Staff Machine Learning Engineer at Babylist, you own personalization and decide where it goes.

So the hard part is yours: what to model, how it should work and whether what shipped actually helped. 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 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. We don't have architects who've stopped building. 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.

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. 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. You've shipped production ML for enough years to have earned strong opinions, and you hold them loosely. You've already changed how a team builds with AI, and the current way stuck.

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.

You measure yourself by impact: a customer outcome, or a model a dozen surfaces come to depend on. You're curious: you spot problems before they're filed and push your own ideas until they ship. 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.

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 review skills and fast CI.

The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts.



Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow Shopify (payments), Iterable (CRM) An engineer, expecting her first baby, couldn't find the registry she wanted.

And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. Remote-first across the US and Canada, and we have been for years. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee.You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship.

You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.

Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values.

Company-paid medical and fully covered dental and vision 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 We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. Official outreach only ever comes from an @babylist.

📌 Machine Learning Engineer (Python) - Medical Research (Winnipeg)
🏢 Babylist -
📍 Winnipeg