Research Scientist, AI, Formal and informal Reasoning

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.

Locations

• Paris, France, FR

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

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