Staff Product Manager, Recommendations & Discovery

Das ist der Job

Requirements Real B2C ML product depth.

Darum lohnt es sich

This is table stakes at Babylist — every team uses AI daily — and we expect you to model what AI‑native PM craft looks like for the team around you Adaptability to change. You jump in where needed, working across team boundaries without waiting for permission.

You are humble, low‑ego, and biased toward action Strongly preferred: Background in e‑commerce or marketplaces; experience helping build or scale an ML personalization function from scratch What the job involves 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 Own recommendations and discovery at Babylist end‑to‑end.

Translate ambiguous business problems into clear technical direction the ML team can act on Set the quality bar for ML‑powered experiences. Give specific, timely feedback that improves the team's output. 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 Strategic foresight.

You can articulate the maturity curve of personalization and discovery at Babylist — where we are, what's next, and the effort behind each step. You hold a strong, opinionated view of the product and you know when to update your priors Deep customer expertise.

This is the irreplaceable PM contribution in a builder world, and it has to be a genuine strength. You talk to customers directly with regularity and bring concrete evidence (qualitative and quantitative) into every decision Commercial ownership. You are fluent in the business.

You understand how recommendations and feed surfaces drive registry completion, GMV, ad revenue, and retention. You can defend a unit economics model and partner with finance and data without needing them to translate. You don't celebrate launches — you own impact Clarity of thought.

You communicate with extreme clarity that moves conversations forward fast. You don't mistake collaboration for consensus AI‑native daily practice. You actively use LLMs and AI coding tools to prototype, analyze, query data, and move faster than you could without them. You have intuition for what current models are good and bad at.

You select for change, not against it. Strategy, KPIs, quality bar, impact, the hard tradeoffs. You are the person the rest of the company looks to when a question about discovery or recommendations has to get answered Set the one‑year horizon for product personalization at Babylist.

Articulate where we should be a year from now, defend the sequence of bets that gets us there, and update with conviction and speed when evidence demands Be a true peer to the ML EM. Set technical and product direction together. Help shape the modeling, data, and evaluation infrastructure that makes the next five years of work possible.

Decide what "good" looks like for a recommendation, and what unacceptable looks like. Make the hard tradeoffs Raise the whole company's judgment about ML investment.

As the company’s definitive voice on ML and recommendations, you'll help leadership develop intuition for where ML compounds — what's table stakes, what's a real lever, and what to avoid. You will make the case for where ML matters and build belief that gets the right bets funded Operate as a AI‑enabled builder.

Use AI‑native workflows in your own work. Stand up prototypes, run your own analyses, and ship things yourself when that's the fastest path to the right answer Mentor and develop the PMs around you. Help raise the bar for the function. Contribute to hiring as the org evolves #J-18808-Ljbffr