Netflix is looking to solve various business problems including personalization algorithms, member understanding, creative tooling, system optimization, and innovative tooling through applied machine learning research.
Requirements
- Experience with machine learning applied to at least one of the following domains: Personalization & Recommender Systems, Natural Language Processing, Computer Vision, Reinforcement Learning, Reliable ML, Multimodal Data, Model Optimization and Efficiency, ML Platform & Infrastructure, General ML Application Engineering, Computer Graphics
- 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 and common challenges like explainability
- Publications in relevant topics in top conferences or journals (for research-based roles)
- Comfortable with distributed computing environments such as Spark or Presto (nice to have)
- Comfortable with software engineering best practices (e.g., version control, testing, code review, etc.) (nice to have)
Responsibilities
- Improving personalization algorithms
- Developing recommender systems using Transformers/LLMs
- Applying natural language processing techniques such as fine-tuning and prompt engineering
- Working on computer vision tasks such as image and video understanding
- Developing reinforcement learning models for offline and online learning
- Implementing reliable machine learning solutions with robustness and explainability
- Building scalable machine learning platforms and infrastructure
Other
- Currently pursuing a Doctorate/ PhD degree in the Machine Learning or related field at an accredited university
- Great communication skills, both oral and written
- Curious, self-motivated, and excited about solving open-ended challenges at Netflix
- Ability to work in a team and collaborate with others
- Must be available for a minimum of 12 weeks for the internship
