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.

NOTE: This job posting is inclusive of a variety of positions within our AI for Member Systems (AIMS) Engineering group. Based on your background, expertise and interests, we will route you to the appropriate team(s). All teams may not be hiring at the same time.

At Netflix, our mission is to entertain the world. That means connecting billions of people with movies, TV shows, and games they’ll love. To do this, we invest in deeply understanding our members and building world-class discovery and personalization experiences. Every time a member turns to Netflix, they’re relying on us to help them find their next favorite story—and delivering on that promise requires state-of-the-art machine learning and personalization models at a global scale.

Applied Machine Learning Research at Netflix drives various aspects of our business, including personalization, recommendations, search, content understanding, messaging, targeting, new member acquisition, evidence, etc. As such, our research spans many Artificial Intelligence and Machine Learning areas, including Large Language Models (LLMs) and other foundation models, deep learning, search and recommender systems, causal inference, reinforcement learning and bandits, computer vision, computer graphics, natural language processing, and computational advertising. 

We are seeking exceptional individuals to join our team as full-time Research Scientists. In this role, you will drive applied research by conceptualizing, designing, implementing, and validating innovative algorithmic solutions. Your work will involve exploring and applying state-of-the-art AI/ML techniques—including LLM pretraining, fine-tuning, and robust offline experimentation—while developing production-ready systems.

To thrive in this position, you should have a solid background in Artificial Intelligence and Machine Learning, strong software engineering skills, a passion for continuous learning, and a genuine enthusiasm for solving real-world problems. Collaboration within multi-disciplinary teams is essential, as are excellent communication and leadership abilities. You should be adept at setting priorities and maintaining a strong execution focus in a dynamic, fast-paced environment.

What we are looking for:

• A strong passion for solving real-world problems at scale through the application of cutting-edge Artificial Intelligence and Machine Learning

• Ph. D or Masters in Computer Science, or any of the related fields

• 6+ years of research experience with a track record of delivering quality results

• Deep expertise in machine learning, including both supervised and unsupervised learning, and practical experience in LLM development.

• Demonstrated success in applying LLMs and other Foundation Models to real-world challenges, preferably with experience in post-training LLMs, including fine-tuning and distillation.

• Strong software engineering skills.  

  • Required: Python, TensorFlow, PyTorch

  • Nice to have: Java, Scala, Spark, Hive, Jax, Flink, Hadoop

• Excellent interpersonal, written, and verbal communication skills

Preferred, but not required:

• Proven experience as a technical leader

• Skilled in collaborating  with cross-functional teams

• Research publications in peer-reviewed journals and conferences on relevant topics

• Hands-on experience in distributed training, reinforcement learning-based training of LLMs, conversational agents, and Personalization

• Proficiency with cloud computing platforms and large web-scale distributed systems

• Applied research experience in industrial settings

• Contributions to open source contributions

• Experience in one or more of the following areas: search, natural language processing, knowledge graphs, conversational agents, personalization, and reinforcement learning

Links:

• <