Staff Machine Learning Engineer

Role overview

Lead the technical direction and hands-on development of recommendations and personalization across a consumer product, including product discovery, a personalized feed, search, and an AI-assisted registry experience. The work combines early-stage model building with decisions about shared architecture, evaluation, and how to measure business impact.

Responsibilities - Set the personalization roadmap and sequence high-impact modeling and platform investments across product surfaces. - Take ambiguous discovery problems from initial exploration through production deployment and measurement. - Build and evolve recommendation approaches, including candidate retrieval, ranking, and representations of product preferences. - Design offline and online evaluation, and assess issues such as cold start, position bias, and popularity bias. - Partner with product, design, data, and engineering to choose problems and shape solutions. - Coach teammates and serve as a machine learning resource across the engineering organization.

Requirements - Experience shipping recommendation or personalization systems to a substantial consumer audience, with evidence of business impact. - Strong understanding of recommender systems, including retrieval and ranking, evaluation, cold start, and bias mitigation. - Fluency with Python machine learning tools and experience owning models through deployment and monitoring. - Ability to contribute to application backend work and make sound architecture choices across multiple product surfaces. - Comfort operating amid ambiguity, choosing pragmatic baselines when appropriate, and building toward more advanced methods. - Experience guiding technical work and helping other engineers grow.

Benefits and work setup - Remote-first work across the United States and Canada, with in-person team gatherings twice a year. - Medical coverage, dental and vision, a 401(k) match, paid parental leave, and a gradual return-to-work program. - Paid year-end company break, remote-work stipend, and mental-health and wellness support.