Applied Machine Learning Intern Jobs in Oregon (NOW HIRING)

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. 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: Netflix Research site Our long term business view 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. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses.

You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000.00 - $750,000.00

This compensation range will vary based on location. 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.