Machine Learning Research at Netflix improves various aspects of our business, including personalization algorithms, member and title understanding, creative tooling, system optimization, and innovative tooling.
Requirements
- Domain expertise in one or more of the following areas: Personalization & Recommender Systems, Natural Language Processing (NLP), Reinforcement Learning (RL), Computer Vision (CV), Computer Graphics, Reliable ML, Causal ML, Agentic AI, Multimodal Data, Model Optimization and Efficiency, ML Platform & Infrastructure, General ML Application Engineering.
- Experience programming in at least one programming language (Python, Java, Scala, or C/C++)
- Familiarity developing ML models using common frameworks (e.g., PyTorch, TensorFlow, Keras) and training on GPUs.
- Familiarity with distributed training and inference paradigms and associated frameworks (eg. DDP, FSDP, HSDP, Deepspeed)
- Familiarity with end-to-end machine learning pipelines (e.g. training or production deployment) and common challenges like explainability.
- Comfortable with distributed computing environments such as Spark or Presto.
- Comfortable with software engineering best practices (e.g. version control, testing, code review, etc.).
Responsibilities
- Using Transformers/LLMs for recommendations, collaborative filtering, content-based recommendation, hybrid systems, and conversational recommenders.
- Large Language Models (LLMs), fine-tuning, in-context learning, prompt engineering, alignment, evaluation, text generation, and embeddings.
- Offline and online RL, alignment and post-training, preference- and human-feedback-based learning, bandit algorithms.
- Image and video understanding, generation, and representation learning.
- 3D modeling and understanding, neural rendering, animation, and related areas.
- Robustness, explainability, interpretability.
- Developing and evaluating agentic systems that reason, plan, and act autonomously, including tool use, retrieval-augmented reasoning, memory and goal management, and feedback-driven learning.
Other
- Currently enrolled student pursuing an advanced degree (PhD) in areas such as Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Economics, Computational Biology, Chemistry, Physics, Cognitive Science or a related field
- Curious, self-motivated, and excited about solving open-ended challenges at Netflix.
- Great communication skills, both oral and written.
- Internships are paid and are a minimum of 12 weeks, with a choice of fixed start dates in January 2026 (Winter), May or June 2026 (Summer) to accommodate varying school calendars.
- This program is intended for students who will be returning to school for at least one semester/quarter following the internship to be eligible for full time employment.
