Engineering Manager - Page Construction Models / Ranking Models

Experience leading applied ML, ML engineering, or applied science teams on large-scale ranking, recommendation, or personalization models. Strong technical depth in recommender systems, ranking, or slate/page-level optimization; comfortable in architecture discussions, model trade-offs, and experimentation strategy with senior engineers. A track record guiding teams through major technical transitions - for example, from traditional ML to deep learning, or from deterministic models to generative, LLM-based approaches. Strong product instincts: the ability to connect technical decisions to member experience outcomes and partner effectively with product management. Excellent stakeholder management and communication skills, able to align senior partners across engineering, science, product, and platform teams. A track record building and leading diverse, high-performing technical teams in a fast-moving, high-autonomy environment. 8+ years in applied ML/science or ML engineering, including 3+ years in a technical leadership or people management role. Experience with applying large language models and genAI innovations recommendation and ranking problems. Experience with multi-objective optimization or slate/page-level value modeling - problems where the quality of a whole set matters, not just individual items. Experience managing teams operating across both mature, production-grade models and early-stage experimental work at the same time. Background at a consumer-scale company with AI-driven products (streaming, social media, marketplaces, search, advertising). Familiarity with the full ML production lifecycle: data pipelines, training, evaluation, serving, and experimentation. Comfortable operating with a high degree of autonomy, building lightweight structure without over-engineering process.