Principal Product Manager, Recommendations & Discovery

We're hiring a Principal PM to own personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building 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.

Responsibilities

Own personalization at Babylist end-to-end; Set the two-year horizon for product personalization; Be a true peer to the ML Engineering Manager and set technical and product direction together; Set the quality bar for ML-powered experiences; Raise the whole company's judgment about ML investment; Operate as an AI-enabled builder; Mentor and develop the PMs around you; Act as the authority on recommendations and discovery at Babylist and partner with the ML Engineering team

Requirements

Demonstrated product leader with meaningful time inside ML-powered consumer products and ownership of recommendations, personalization, or discovery strategy end-to-end at scale; Held Principal PM, GPM, Director, or comparable roles with org-level influence; Real B2C ML product depth with experience shipping recommendations, search, ranking, or personalization systems in a consumer-facing product and fluency in candidate generation vs ranking, online vs offline evaluation, cold start, exploration vs exploitation, novelty effects, and tradeoffs between business objectives and user-perceived relevance; Real technical fluency with ML systems including understanding data pipelines, feature engineering, model training, deployment, monitoring, and iteration; Comfortable reading model design docs and being a peer to a senior ML Engineering Manager; Builder's instinct for early-stage ML including knowing when a rule beats a model, when a model needs a guardrail, and when a hard-coded baseline is the right first step; Strategic foresight to articulate the maturity curve of personalization and discovery and update priors with conviction and speed; Deep customer expertise with regular direct customer contact and concrete qualitative and quantitative evidence; Commercial ownership with fluency in how recommendations and feed surfaces drive registry completion, GMV, ad revenue, and retention; Ability to defend a unit economics model and partner with finance and data; Clarity of thought and communication; AI-native daily practice with active use of LLMs and AI coding tools; Adaptability to change with low ego and bias toward action

Benefits

Company-paid medical, dental, and vision insurance; Retirement savings plan with company matching and flexible spending accounts; Generous paid parental leave and PTO; Winter Wonder Week (company-wide paid week off at the end of the year); Remote work stipend; Perks for physical, mental, and emotional health, parenting, childcare, and financial planning; Target 22.5% annual bonus; Competitive equity; Access to leading AI tools like Claude and ChatGPT from day one; Twice-a-year company gatherings; Meaningful opportunities for career advancement