Research Scientist, FAIR Core Learning and Reasoning

Meta·Paris, Île-de-France, France·posted 21d ago · last seen 52m ago

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About 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.

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Description

About

Meta's Fundamental AI Research organization is seeking a Research Scientist to drive advancements in generative models, with a particular focus on fundamental topics (data efficiency, continual learning) in large language models (LLMs) research. The role involves working across the full spectrum of research, engineering, and deployment for both product and frontier model efforts.

Responsibilities

  • Lead and execute research to advance 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

Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • PhD in Computer Science, Mathematics, or a related quantitative discipline
  • Hold first-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR)
  • Demonstrated experience with training, fine-tuning, and experimentation on foundation models beyond black-box usage
  • Familiarity with PyTorch
  • Must be able to obtain and maintain work authorization in the country of employment Ability to conduct independent research, Experience communicating research findings and complex technical ideas through publications, presentations, or design documents to both technical and non-technical stakeholders

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