Engineering Manager, Core Applications - AI for Member Systems

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

The Opportunity

Netflix's mission is to entertain the world by connecting members with stories they'll love. With millions of members around the globe, our product helps people quickly find something great to watch. The AI for Member Systems (AIMS) organization sits at the core of this experience, building and operating the AI systems that power recommendations, personalization, search, discovery, and messaging.

Fast-paced innovation in large language models (LLMs) and generative AI is reshaping how we personalize and surface content. Within AIMS, the AI Applications Coreteam is a horizontal applied science and ML engineering team that builds foundational capabilities used across multiple product surfaces. While other AIMS teams focus on specific experiences (Recommendations, Search, Messaging), Core builds the shared components that power all of them:

• Reward models that define "good" outcomes for member experiences (engagement, satisfaction, discovery, long-term value)

• Entity/metadata libraries that unify how models reason about content across different surfaces

• LLM post-training frameworks tailored to personalization use cases

• Utility optimization systems that help balance multiple objectives at scale

We're looking for a senior engineering leader to lead and grow this team—someone who can build generalizable tools, APIs, and frameworks that multiple application teams adopt, while navigating the tension between near-term product needs and long-term platform investments.

In This Role, You Will

• 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.

What We're Looking For

• 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.

Preferred Qualifications

• 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