Research Scientist, AI, Formal and informal Reasoning
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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.
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About
Meta is seeking a Research Scientist to join its Fundamental AI Research (FAIR) organization, focused on making significant advances in LLMs reasoning, using reinforcement learning, synthetic data generation and advanced scaffolding/agentic techniques, partially with a focus on formal and informal maths. 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, important resources, and research facilities.
Responsibilities
- Lead, collaborate, and execute on research that pushes forward the state of the art in reasoning research, with an initial focus on formal and informal mathematical reasoning
- Work towards long-term high-stakes 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. Play a significant role in healthy cross-functional collaboration
Minimum Qualifications
- Holds a PhD in the field of Computer Science, Mathematics, or similar quantitative field
- Experience training and evaluating large models on State-of-the-Art codebases and developing new architectures, losses and training recipes
- 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 Reinforcement Learning codebases and familiarity with one or more Machine 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 Familiarity with the Lean 4 language and ecosystem and mathematical expertise
- Experience with communicating complex research for public audiences of peers