Engineering Manager, Core Applications - AI for Member Systems

Netflix is a company dedicated to entertaining the world by connecting members with stories they'll love. They are seeking a senior engineering leader to lead and grow the AI Applications Core team, which builds foundational capabilities for personalization across multiple product surfaces.

Responsibilities:

• Lead a senior applied science and ML engineering team that builds foundational personalization capabilities used by multiple product teams
• Set the vision and roadmap for shared reward models, entity/metadata libraries, embedding utilities, and LLM post-training for personalization
• Prioritize horizontal investments that create leverage across recommendations, search, messaging, and emerging GenAI experiences
• Design and evolve APIs, abstractions, and integration patterns so downstream teams can adopt shared components while the Core team iterates on internals
• Extend and generalize reward models to new content types and surfaces
• Stand up and tune online utility modeling and optimization layers (e.g., bandits, policy learning, multi-objective optimization) that balance engagement and long-term member value
• Shape shared post-training and alignment utilities for member-facing LLMs (e.g., supervised fine-tuning, RLHF/RLAIF) used in ranking, discovery, search, and messaging
• Partner closely with AIMS foundations, product application teams, and ML platform/infra teams to ensure Core capabilities are aligned, integrated, and widely adopted
• Hire, develop, and retain a diverse, high-caliber team of ML engineers and applied scientists, fostering an environment where senior talent can do their best work

Requirements:

• Experience leading applied ML, ML engineering, or applied science teams working on large-scale personalization, ranking, marketplace optimization, or related decision systems
• Strong background in applied ML and recommender systems, including rewards, multi-objective optimization, and/or long-term value modeling
• Demonstrated success driving horizontal or platform-like ML efforts where impact is measured by adoption and leverage across multiple teams
• Proven ability to design and ship APIs, libraries, and reusable components that product teams can easily adopt and extend
• Strong communication and influence skills; able to align senior partners across engineering, science, and product
• Track record of building and leading diverse, high-performing technical teams in a fast-moving, high-autonomy environment
• 8+ years in applied ML/science or ML engineering, with 3+ years in a technical leadership or people management capacity
• Familiarity with modern LLM/GenAI applications and post-training approaches (e.g., fine-tuning, RLHF/RLAIF, evaluation pipelines) in production settings
• Experience acting as a bridge between foundational/platform teams and product application teams—translating capabilities into usable components while feeding requirements back into foundations