Postdoctoral Researcher (phd) - Fair - Core Learning

Meta · Big Tech · Paris, France

Postdoctoral researcher in Meta's FAIR organization focusing on generative models, specifically Reinforcement Learning in LLMs and/or formal math (Lean). Responsibilities include leading research, experimental design, implementation, evaluation, publication, and collaboration.

What you'd actually do

Innovate, lead, and execute pioneering research to push the state-of-the-art in generative models and LLM performance
Systematically perform independent research, quickly adapting to new developments in the field
Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
Contribute to publications, open-sourcing initiatives, and mentor other team members
Ensure effective cross-functional collaboration

Skills

Required

PhD in Computer Science, Mathematics, or a similar quantitative discipline
Training foundation models
Fine-tuning foundation models
Experimentation on foundation models
Reinforcement Learning for LLMs
Lean (formal mathematics)

Nice to have

publication record

What the JD emphasized

PhD in Computer Science, Mathematics, or a similar quantitative discipline
training, fine-tuning, and experimentation on foundation models beyond black-box usage
Expertise in RL for LLMs and/or Lean (formal mathematics)
Holds first-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR)

Other signals

pioneering research
state-of-the-art
foundation models
Reinforcement Learning
LLMs

Read full job description

Meta's Fundamental AI Research (FAIR) organization is seeking a postdoctoral researcher to drive advancements in generative models, with a particular focus on Reinforcement Learning in large language models (LLMs) and/or formal math (Lean).

Responsibilities

Innovate, lead, and execute pioneering research to push the state-of-the-art in generative models and LLM performance Systematically perform independent research, quickly adapting to new developments in the field Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results Contribute to publications, open-sourcing initiatives, and mentor other team members Ensure effective cross-functional collaboration

Qualifications

Currently possess or be pursuing a PhD in Computer Science, Mathematics, or a similar quantitative discipline Demonstrated experience with training, fine-tuning, and experimentation on foundation models beyond black-box usage Must be able to obtain and maintain work authorization in the country of employment Expertise in RL for LLMs and/or Lean (formal mathematics) Holds first-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR)