Staff Product Manager, Recommendations & Discovery

What the Role Is We're hiring a Staff Product Manager to own personalization and discovery across Babylist's consumer experience — the homepage feed, product recommendations, and the ML-powered systems that make the registry building journey feel effortless. Babylist was built on editorial recommendations — products chosen by humans with deep baby gear expertise – which are an important part of the foundation of the trust we've earned with millions of families. We now have the remit to build on our editorial strength; using one of the richest first-party datasets in parenting to layer personalized, ML-powered recommendations across every consumer decision point.

We are early on the journey, have a real mandate, and need a product leader who has seen ML personalization done at scale to come define what great looks like for Babylist. If you're looking to step into a mature ML organization and optimize on the margins, this isn't the right role. If you've worked inside a strong ML personalization team, learned what good looks like, and want to bring that knowledge to a company early on in this journey — with the leverage to shape what we build and how we build it — read on.

Registry building is the heart of the Babylist product — every parent builds a list of dozens of products, from stroller to swaddle, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). That makes registry building one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, and a user who genuinely wants help. We're only beginning to build on that opportunity.

This role is the authority on recommendations and discovery at Babylist. You hold the quality bar, set the one-year horizon, and operate as the foremost expert on the space inside the company. You shape how the whole company thinks about personalization.

You partner closely with our ML Engineering team — opinionated about model behavior, fluent in tradeoffs between business goals and user value, and able to hold real conversations about retrieval, ranking, candidate generation, and evaluation.

Who You Are

You are a demonstrated product leader who has spent meaningful time inside ML-powered consumer products. You have owned a recommendation, personalization, and/or discovery surface end-to-end at scale — and you have the scar tissue to prove it. You have held Senior PM, Staff PM, GPM, or comparable Lead roles.

You're motivated by the chance to bring what you've learned to a company that's earlier in this journey than you've been before, and you see that as an asset, not a downgrade. You bring: Real B2C ML product depth. You have shipped recommendations, search, ranking, or personalization systems in a consumer-facing product.

You can speak fluently about candidate generation vs. ranking, online vs. offline evaluation, cold start, exploration vs.

exploitation, novelty effects, and the tradeoffs between business objectives and user-perceived relevance. You know the failure modes and the diligence required to ship ML responsibly. Real technical fluency with ML systems.

You don't write production model code, but you understand the full ML lifecycle — data pipelines, feature engineering, model training, deployment, monitoring, and iteration. You're comfortable reading a model design doc, pushing back on architectural choices when the product reality demands it, and being a true peer to a senior ML EM rather than a translator. A builder's instinct for early-stage ML.

You know that early ML investment is about getting the right reps on a small number of bets, not shipping breadth. You understand when a rule beats a model, when a model needs a guardrail, and when a hard-coded baseline is the right first step. You'd rather ship one excellent recommender and learn from it than launch six mediocre ones.