Staff Machine Learning Engineer

Join Babylist, a leading parenting resource, as a Staff Machine Learning Engineer. In this role, you will lead the technical direction for recommendations and personalization across Babylist's consumer experience. You will work closely with a PM, Engineering Manager, Senior MLE, and fullstack software engineers to shape the future of personalization on the Discovery team. Your work will have a direct impact on the user experience and business metrics.

Assumer la responsabilité des recommandations et de la personnalisation de l'expérience client de Babylist, en utilisant des données de première partie.

Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend

About Babylist

If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on

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. 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

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

Responsibilities

• Diriger l'équipe de découverte dans le développement et l'optimisation des modèles de récupération et de réévaluation.
• Collaborer avec les équipes produit, design et données pour façonner les fonctionnalités à construire et assurer leur succès.
• An ability to take an ambiguous problem and start moving before anyone hands you the full picture
• 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

Requirements

• Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias
• 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