[Remote] Research Scientist Intern, FAIR - Multimodal LLM (PhD)
Note: The job is a remote job and is open to candidates in USA. Meta is seeking Research Interns to join Fundamental AI Research (FAIR) Multimodal Foundations teams, committed to advancing the field of Artificial Intelligence. The role involves conducting research, developing novel solutions, and contributing to Meta's product development in areas such as computer vision and multimodal AI.
Responsibilities
- Conduct research on advanced topics in video and image generation and understanding
- Brainstorm with research mentors, review literature and existing solutions of a challenging real-world research problem
- Develop novel solutions, implement prototypes, and perform extensive experiments for large-scale vision understanding, to test the proposed solutions in meaningful benchmarks and metrics, analyze the results and verify the conclusions
- Draft and polish research reports and/or publications
- Present research outcomes to internal and/or external audiences
- Contribute research that can be applied to Meta product development
Skills
- Solid research and project experience involving the application of computer vision algorithms and deep learning methods
- Proficiency in deep learning frameworks such as PyTorch
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
- Experience advancing AI techniques in Computer Vision and/or Machine Learning
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops or conferences in Computer Vision (CVPR, ECCV, ICCV) or Machine Learning (NeurIPS, ICML, ICLR)
- Experience in utilizing theoretical and empirical research to solve problems
- Experience working and communicating cross functionally in a team environment
- Intent to return to a degree-program after the completion of the internship/co-op
- Experience working with large-scale datasets and compute infrastructure
- A strong track record of contributions to open-source projects, benchmarks, or shared research artifacts
Benefits
- Benefits
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