At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next. 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. 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. Our research spans many areas of machine learning, including recommender systems, reinforcement learning, computer vision, natural language processing, optimization, causality, and operations research. Great applied research also requires robust machine learning infrastructure, another strong emphasis at Netflix. We are looking for individuals with the following qualifications:

• Currently enrolled student pursuing a PhD (2nd-4th year preferred) in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Economics, Computational Biology, Chemistry, Physics, Cognitive Science, or a related field
• Available to work onsite, 40 hours per week in Los Gatos (housing/relocation support provided)
• Domain expertise in one or more of the following areas:

Personalization & Recommender Systems:

Transformers/LLMs for recommendations, collaborative filtering, content-based recommendation, hybrid systems, conversational recommenders

• Natural Language Processing (NLP): Large Language Models, fine-tuning, in-context learning, prompt engineering, alignment, evaluation, text generation, embeddings
• Computer Vision (CV): Image and video understanding, generation, and representation learning

Reliable ML:

Robustness, fairness, uncertainty quantification, explainability/interpretability

Causal ML:

Causal inference, causal discovery, double ML, policy learning, dynamic panel/choice modeling

Agentic AI:

LLM agents, tool use, retrieval-augmented reasoning, memory and goal management, multi-step reasoning

Multimodal Data:

Modeling across text/image/video/audio, modality fusion and alignment, multimodal retrieval

Model Optimization & Efficiency:

Training/inference efficiency, model benchmarking, compression, distillation

• Experience programming in at least one language (Python, Java, Scala, or C/C++)
• Familiarity developing ML models with common frameworks (PyTorch, TensorFlow, JAX) and training on GPUs
• Familiarity with distributed training/inference paradigms and frameworks (e.g., DDP, FSDP, HSDP, DeepSpeed)
• Familiarity with end-to-end ML pipelines (training or production deployment) and common challenges like explainability
• Curious, self-motivated, and excited about solving open-ended challenges at Netflix
• Strong written and verbal communication skills Nice to have:
• Publications in top conferences or journals (NeurIPS, ICML, ICLR, RecSys, ACL/EMNLP/NAACL, AAAI, CIKM, WWW, UAI, CVPR)
• Comfort with software engineering best practices (version control, testing, code review) Program details:
• 12-week minimum internship with a start date early January 2027
• Based at our Los Gatos, CA headquarters
• Intended for students returning to school for at least one semester/quarter after the internship; conversion/return offers are based on business need and headcount, and are not guaranteed For your application to be considered complete:
• You will be sent an Airtable form shortly after you submit your application on our careers site; your application will not be considered complete until you fill out and submit this form.
• Include a Resume or CV with complete contact information (email, phone, mailing address) and a list of relevant coursework and publications (if applicable). You will be asked to include a short statement describing your research experiences and interests, and (optionally) their relevance to Netflix Research. For inspiration, have a look at the Netflix Research site.
• Applications will be reviewed on a rolling basis and it's in the applicant's best interest to apply early.

The application window will remain open until roles are filled. About the Internship Program At Netflix, we offer a personalized experience for interns, and our aim is to offer an experience that mimics what it is like to actually work here. We match qualified interns with projects and groups based on interests and skill sets, and fully embed interns within those groups. Netflix is a unique place to work and we live by our values, so it's worth learning more about our culture.

• Internships are paid and are a minimum of 12 weeks, with a fixed start date early January 2027 (Winter). Our Winter internships will be located at our headquarters in Los Gatos, CA.
• 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.

Conversion or return offers are based on business need and headcount, and are not guaranteed. At Netflix, we carefully consider a wide range of compensation factors to determine the Intern top of market. We rely on market indicators to determine compensation and consider your specific job, skills, and experience to get it right. These considerations can cause your compensation to vary and will also be dependent on your location. The overall market range for Netflix Internships is typically $40/hour - $85/hour. This market range is based on total compensation (vs. only base salary), which is in line with our compensation philosophy. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Apply now and help us shape the future of entertainment at Netflix! Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.