About the job
We're hiring two engineering managers to lead the teams behind two of the most important algorithms in the recommendations space. The first team owns homepage construction: deciding which sections appear on a member's page, in what order, and how they're arranged. The second team owns title ranking: the underlying prediction of how relevant a given title is to a given member.
Responsibilities
Lead and grow a team of AI research scientists and AI research engineers focused on either page construction or title ranking (team assignment determined through the interview process).
Set the technical vision and roadmap for your team, balancing investment across mature, production-grade models and newer generative approaches.
Guide your team through the shift from traditional machine learning toward generative, LLM-based methods.
Drive infrastructure decisions in partnership with adjacent ML and platform teams - including serving infrastructure, foundation model integration, and experimentation tooling.
Own the quality of your team's algorithms across the entire product surface: the main homepage, kids' profiles, partner devices, short-form video, games, and new formats as they emerge.
Partner closely with the engineering manager leading the adjacent team (Page Construction or Ranking) to ensure the two models work together as one coherent personalization experience.
Work closely with Product Management to translate member experience goals into strategy and experimentation plans.
Hire, develop, and retain a diverse, high-caliber team, supporting existing technical leads in an environment where senior talent can do its best work.
Qualifications
Minimum
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.
Preferred
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.