Research Scientist 5 — Content Representation Models (CRM)

Netflix•Los Gatos, CA

2d•$466,000 - $750,000

About The Position

The Content Representation Models team creates a single, unified "language" for Netflix's entire library by developing foundation models that understand everything from video and audio to text and artwork at a semantic level. By treating these powerful embeddings as a core product, we give Netflix the ability to match the right content to the right member, supercharging personalization and helping everyone discover something they'll love. The team's current areas of focus include: Unified Content Embedding: Merging media-based and metadata-based embedding approaches into a single cohesive model, creating rich semantic representations of all content across video, audio, and text modalities Multimodal and Multi-Granularity Embeddings: Creating embeddings from various content types at different levels of detail, from entire shows down to individual shots and clips Semantic IDs: Developing unique, meaningful identifiers for content that enable more sophisticated retrieval and recommendation Profile and Content Embedding Alignment: Aligning member profile embeddings with content embeddings in the same space to enhance personalization We are looking for a Research Scientist specializing in embeddings and representation learning to investigate how we can enhance content understanding capability in Netflix's foundation models. How foundation models understand content is one of the most important open research questions for Netflix personalization. Today, our models rely on a mix of metadata, behavioral signals, and media-based representations. The opportunity ahead is to significantly deepen that understanding through approaches like Semantic IDs, continuous pre-training, novel representation learning methods, or other state-of-the-art techniques. The person in this role will help shape that research direction and bring new ideas to the table. This is an area where the optimal strategy is still being defined, which means there is real room to influence the approach and make a lasting impact on how Netflix's foundation models reason about content. What makes this role unique: Open research problem with real product impact. Enhancing how foundation models understand content is a crucial and unsolved challenge. Your work will directly improve how 300M+ members discover content. Research that ships. This isn't a pure research lab. Your work will feed into foundation models that power personalization across every Netflix surface. The loop between research and member impact is tight. Bring your own approach. We have hypotheses (Semantic IDs, continuous pre-training, etc.) but we're looking for someone who brings their own perspective and methods to the problem. World-class collaborators. You will work alongside researchers and engineers across content understanding, foundation models, and application teams who are pushing the state of the art in personalization at scale.

Requirements

Ph.D. in Computer Science or a related field with a strong publication record in embeddings, representation learning, or a closely related domain
3+ years of research experience with a track record of delivering quality results
Deep expertise in machine learning, including practical experience with LLMs and/or foundation models
Strong software engineering skills in Python (eg, PyTorch /)
Excellent communication and collaboration skills

Nice To Haves

Experience in adopting LLM for Recsys. More specifically, building Semantic IDs and ground them in LLMs.
Experience in computer vision or multimodal AI
Industry experience in recommendation systems, search, personalization, or retrieval
Experience with LLM pre-training, fine-tuning, or distillation
Hands-on experience with distributed training
Publications in top ML conferences (NeurIPS, ICML, ICLR, KDD, RecSys)
Applied research experience in industrial settings

Responsibilities

Drive applied research on enhancing content understanding capability in Netflix's foundation models
Conceptualize, design, implement, and validate new approaches to representation learning and content embeddings
Explore and apply state-of-the-art AI/ML techniques, including methods for improving how LLMs and foundation models represent and reason about content
Develop production-ready solutions and partner with application teams to ensure research translates into member-facing impact
Design and run rigorous offline experiments and evaluations to validate new approaches
Collaborate with cross-functional teams across content understanding, foundation models, and personalization applications
Contribute to the broader research community through publications at top venues