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Description
About Netflix
Netflix is a global entertainment service with 278 million paid memberships in over 190 countries. They offer a wide variety of TV series, films, and games across various genres and languages. Members can enjoy unlimited streaming, pausing, and resuming content anytime, anywhere, and have the flexibility to change their plans at any time.
Description
Machine Learning Intern
Netflix is revolutionizing the entertainment industry through innovation in content production, streaming technology, and personalization. Applied Machine Learning Research at Netflix focuses on areas such as:
• Personalization algorithms
• Member understanding
• Creative tooling
• System optimization
• Innovative tooling
The research encompasses various machine learning fields, including:
• Recommender Systems
• Reinforcement Learning
• Computer Vision
• Natural Language Processing
• Optimization
• Causality
• Operations Research
Interns will be assessed for suitability in one of the following areas:
• Research
• Infrastructure
• Engineering
Currently pursuings:
• Currently pursuing a Doctorate/PhD in Machine Learning or a related field at an accredited university.
• Experience with machine learning applications in at least one of the following domains:
• Personalization & Recommender Systems: Transformers/LLMs, multi-modal recommenders, collaborative filtering, content-based recommendation, hybrid systems, 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 ML model development and deployment.
• General ML Application Engineering: Implementing ML solutions across various domains.
• Computer Graphics: 3D modeling and understanding, neural rendering, animation, and related areas.
• Programming experience in at least one 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 ML pipelines (e.g., training or production deployment) and common challenges like explainability.
• Publications in relevant topics in top conferences or journals (for research-based roles).
• Curious, self-motivated, and passionate about solving open-ended challenges.
• Excellent communication skills (oral and written).
Nice to Have:
• Experience with distributed computing environments like Spark or Presto.
• Familiarity with software engineering best practices (e.g., version control, testing, code review).
Application Process:
• Submit your application through the Netflix careers site.
• Complete the Airtable form sent after submitting your application.
• Include your resume/CV with contact information and a list of relevant coursework and publications.
• In the Airtable form, select your primary (or secondary) ML area for the internship.
• Provide a short statement describing your research experiences and interests (and their relevance to Netflix Research).
Internship Details:
• Paid internships for a minimum of 12 weeks.
• Fixed start dates in May or June 2025.
• Location: Los Gatos, CA or Los Angeles, CA offices.
• Compensation: Typically $40/hour – $110/hour (based on total compensation).
Diversity & Inclusion:
Netflix is an equal opportunity employer and values diversity. They 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.