About the job
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.
Responsibilities
Drive applied research by conceptualizing, designing, implementing, and validating innovative algorithmic solutions. Explore and apply state-of-the-art AI/ML techniques, including LLM pretraining, fine-tuning, and robust offline experimentation. Develop production-ready systems. Collaborate within multi-disciplinary teams. Set priorities and maintain a strong execution focus in a dynamic, fast-paced environment.
Qualifications
Minimum
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
Excellent interpersonal, written, and verbal communication skills
Preferred
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
Nice to have: Java, Scala, Spark, Hive, Jax, Flink, Hadoop