Staff Machine Learning Engineer

Join Babylist, a leading parenting resource, as a Staff Machine Learning Engineer. In this role, you will own recommendations and personalization across Babylist's consumer experience, including the homepage feed, product recommendations, search, and the ML-powered systems that make registry building effortless. You will set the technical direction for personalization, work alongside a PM, Engineering Manager, Senior MLE, and fullstack software engineers, and shape the future of Babylist's personalization efforts.

• Assumer la responsabilité de la direction technique des modèles de personnalisation et de recommandation, en définissant la stratégie et la feuille de route pour l'évolution des modèles.
• Collaborer avec les équipes produit, design et données pour identifier les opportunités d'amélioration des systèmes de recommandation et de personnalisation, et pour définir les priorités de développement.
• Encadrer et coacher les ingénieurs de l'équipe, en partageant votre expertise en apprentissage automatique et en contribuant à l'amélioration continue des pratiques de l'équipe.

• You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work
• Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias
• AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next
• You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey
• An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue
• A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings
• Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend
• An ability to take an ambiguous problem and start moving before anyone hands you the full picture