- Meta
- Paris, IDF
- Full-Time
- 1 week ago
Meta
Research Scientist, AI, Formal and informal Reasoning: our view in 3 lines...
- The Role: An advanced AI research position to lead and execute research on reasoning in large language models with emphasis on formal and informal mathematical reasoning.
- The Person: Lead and perform experiments to advance reasoning in LLMs by designing experiments, implementing reusable code, running evaluations, contributing publications and mentoring team members.
- Requirements: Holds a PhD, experience training and evaluating large models, first-author publications at NeurIPS/ICML/ICLR, experience with reinforcement learning codebases and familiarity with pytorch and VERL and familiarity with Lean 4 is preferred.
Job Description
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
Preferred Qualifications
- Familiarity with the Lean 4 language and ecosystem and mathematical expertise
- Experience with communicating complex research for public audiences of peers
Key Skills
Key Skills in dark blue have been inferred based on similar industry roles
