AI Research Scientist
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Get Started FreeAbout interviewing at Meta
Recruiter screen, a technical screen (60-minute asynchronous challenge or paired live coding), a short culture-fit questionnaire, then a virtual onsite of about four rounds: fast-paced coding with multiple problems per round, system design scoped to level, a lighter behavioral, and an AI-enabled coding round assessing how you work with a coding assistant.
Read the full Meta interview process →Description
About
Meta is seeking a Research Scientist to join its Fundamental AI Research (FAIR) organization, focused on making significant advances in LLMs reasoning with specific focus on reinforcement learning, informal and formal math and advanced scaffolding/agentic techniques. You will have the opportunity to work with a broad and highly interdisciplinary team of scientists, engineers, and cross-functional partners, and will have access to cutting edge technology, resources, and research facilities.
Responsibilities
- Lead, collaborate, and execute on research that pushes forward the state of the art in LLM reasoning
- Work towards long-term ambitious research goals, while identifying intermediate milestones
- Directly contribute to experiments, including designing experimental details, implement reusable code, running evaluations, and organizing results
- Contribute to publications and open-sourcing efforts
- Mentor other team members and actively contribute to cross-functional collaboration
Minimum Qualifications
- Currently has or is in the process of obtaining a PhD in Computer Science, Mathematics, or a similar quantitative field
- First-author publications at peer-reviewed AI conferences (e.g. NeurIPS, ICML, ICLR)
- Experience in training, fine-tuning, and/or experimenting with foundation models beyond black-box use
- Experience working with SOTA RL codebases and familiarity with one or more deep learning frameworks (e.g. pytorch, VERL, …)
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience with LEAN