Meta is committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world.
Requirements
- Expertise in areas relevant to the internship topics such as agent modeling, reinforcement learning, code generation, computational world modeling, language modeling, planning, agentic scaffolds, memory systems or others, as demonstrated by publications, coursework, blog posts, prior internships, and other relevant contributions
- Experience with Python and deep learning frameworks such as Pytorch or Tensorflow, as demonstrated by course projects, open source releases, or similar venues
- Experience with interpreting deep neural networks mechanistically, correlating their observable behavior with properties of model parameters and activations
- Demonstrated software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
Responsibilities
- Perform research to advance the science and technology of intelligent machines
- Develop new methods and systems for AI agents, reinforcement learning, planning, model based reward modeling and related problems
- Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results
Other
- Currently is in the process of obtaining a PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or relevant technical field
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
- Intent to return to a degree program after the completion of the internship
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, CVPR, ICCV, ECCV, ICLR, ICML, ACL, NAACL, EMNLP, or similar
- Experience working and communicating cross functionally in a team environment
