Netflix is one of the world's leading entertainment services, with 283 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.

Netflix is reinventing entertainment from end to end. We are revolutionizing how shows and movies are produced, pushing technological boundaries to efficiently deliver streaming video at a massive scale over the internet, and continuously improving the personalization of how entertainment is presented to our more than 280 million members around the globe.

Applied Machine Learning Research at Netflix improves various aspects of our business, including personalization algorithms, member 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.

Machine learning interns will be evaluated to find the best fit in one of the Machine Learning areas including various areas of Research as well as Infrastructure and Engineering. See here for a detailed list of areas. Applicants are encouraged to express their interest in one or multiple types of internships listed if your skills and qualifications are aligned.

We Are Looking For Individuals With The Following Qualifications

Currently pursuing a Doctorate/ PhD degree in the Machine Learning or related field at an accredited university
Experience with machine learning applied to at least one of the following domains:
- Personalization & Recommender Systems: Using Transformers/LLMs for recommendations, multi-modal recommenders, collaborative filtering, content-based recommendation, hybrid systems, and conversational recommenders.
- Natural Language Processing (NLP): Large Language Models (LLMs), fine-tuning, in-context learning, prompt engineering, alignment, evaluation, text generation, and embeddings.
- Computer Vision (CV): Image and video understanding, generation, and representation learning.
- Reinforcement Learning (RL): Offline and online RL, alignment and post-training, preference- and human-feedback-based learning.
- Reliable ML: Robustness, explainability/interpretability, causality.
- Multimodal Data: Handling and integrating text, image, video, audio, and other data sources.
- Model Optimization and Efficiency: Training and inference efficiency, model benchmarking, optimization techniques.
- ML Platform & Infrastructure: Building scalable systems for the development and deployment of machine learning models.
- General ML Application Engineering: Implementing machine learning solutions across various domains.
- Computer Graphics: 3D modeling and understanding, neural rendering, animation, and related areas.
Experience programming in at least one programming language (e.g., Python, Java, Scala, or C/C++).
Experience developing ML models using common frameworks (e.g., PyTorch, TensorFlow, Keras) and training on GPUs.
Familiarity with end-to-end machine learning pipelines (e.g. training or production deployment) and common challenges like explainability.
For research-based roles, publications in relevant topics in top conferences or journals.
Curious, self-motivated, and excited about solving open-ended challenges at Netflix.
Great communication skills, both oral and written.

Nice To Have

Comfortable with distributed computing environments such as Spark or Presto.
Comfortable with software engineering best practices (e.g. version control, testing, code review, etc.).

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).
In the Airtable form, you will be asked to select a primary (or secondary) ML area for your potential internship. This will be used to map your application to particular teams & projects.
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.

Don’t meet every single requirement? That’s ok! If you’re excited about this role, but your experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be the right candidate for this or other roles.

Internships at Netflix

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 for the summer. 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, and internships will be located in our Los Gatos, CA office, or in our Los Angeles, CA office, depending on the team.

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 - $110/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.

We are an equal opportunity employer and celebrate diversity, recognizing that diversity of thought and background 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.

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.

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