Engineering Manager, Machine Learning, Content Representation Models (AI Foundations)

Netflix•New York, NY

4d•$190,000 - $920,000

About The Position

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time. The Opportunity At Netflix, innovation in foundation models is redefining how we understand stories and personalize experiences for hundreds of millions of members worldwide. The Content Representation Models (CRM) team sits at the center of this transformation, creating unified representations of content that power personalization and discovery across Netflix. This team develops the embedding models that understand our catalog at every level, from rich metadata-based representations (e.g., tags, talent, synopsis, IP) to media-based representations learned directly from video, audio, and text. Together, these representations form the backbone of how we represent content in our algorithms. As Netflix expands into new content types and formats: from live and interactive experiences to games and podcasts - the need for deeper semantic understanding of our content becomes even more critical. Large vision and language models (VLMs, MLLMs, and GenAI systems) now make it possible to understand media at a far more nuanced and fine-grained level, and this team is bringing those capabilities into the heart of personalization, helping our algorithms reason about stories, style, and audience engagement. These embeddings are foundational not only to personalization and search, but also to applications across Ads, Content & Studio Data Science, and other business verticals: driving insights, optimization, and creativity at scale. We are looking for a seasoned engineering leader to guide this world-class team of ML researchers and engineers. You will lead the evolution of Netflix's content embedding ecosystem — advancing the science of multimodal and metadata-based representation learning, ensuring reliability and adoption in production, and partnering to deliver impact. Questions Your Team Will Help Answer The Content Representation Models team provides a unified, semantically rich representation of content, enabling more engaging recommendations and creative insights. Some of the strategic questions the team answers include: How can we represent every title, episode, and clip, across video, audio, text, and metadata, in a shared embedding space? How can unified content representations enable personalization and discovery across Netflix products? How do we measure and improve the semantic and engagement quality of our embeddings? What foundation architectures (e.g., multimodal LLMs, ensemble embeddings) best capture the richness of Netflix content? How do we ensure our embedding pipelines are reliable, reproducible, and production-grade?

Requirements

Proven track record of building and leading high-performing ML research or foundation model teams.
Experience developing and/or deploying large-scale representation learning, multimodal, or embedding systems.
Strong technical judgment in foundation model architecture, training, evaluation, and deployment.
Experience balancing research exploration with production reliability.
Strong collaboration and communication skills - able to represent technical work to non-technical partners.
Demonstrated ability to drive cross-functional adoption of platform-level ML capabilities.
Advanced degree in Computer Science, Machine Learning, or a related quantitative field, or equivalent practical experience.

Nice To Haves

8+ years of experience in ML or AI, including 3+ years leading technical teams.
Background in multimodal learning, contrastive training, or LLM-based embedding architectures.
Experience building ML systems used across multiple product surfaces.
Experience with large-scale distributed training, embedding infrastructure, or evaluation frameworks.
Familiarity with both research-oriented and production-grade ML workflows.

Responsibilities

Lead a team of researchers and engineers advancing both media-based and metadata-based representations of content.
Drive innovation in foundation model research, leveraging large language and vision-language models to enhance metadata and media understanding, and using Netflix's unique long-form media and engagement data to create richer, more personalized representations.
Ensure production excellence across embedding pipelines - maintaining reliability, performance, and scalability.
Guide adoption of embeddings as first-class products, partnering with downstream application teams to maximize business and product impact.