Research Scientist: Core Learning & Reasoning for LLMs

Meta; Paris; On-site; EUR 90,000 - 150,000; Full time

Job summary

Meta's Fundamental AI Research organization seeks a Research Scientist in Paris to advance generative models and LLM performance across research, engineering, and deployment efforts for product and frontier models.

You will lead independent research, design experiments, implement reusable code, evaluate results, and contribute to publications and open-source work, while mentoring teammates and coordinating with other teams.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or related field; equivalent practical experience.
• PhD in Computer Science, Mathematics, or related quantitative discipline.
• First-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR).
• 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.

Responsibilities

• Lead and execute research to advance generative models and LLM performance.
• Systematically perform independent research and adapt to new developments.
• Contribute to the experimental process: design details, implement reusable code, run evaluations, organize results.
• Contribute to publications, open-sourcing initiatives, and mentor team members.
• Ensure effective cross-functional collaboration.

Skills

First-author publications

Independent research

Experiment design

Education

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

Tools

PyTorch

Job description

Meta's Fundamental AI Research organization seeks a Research Scientist in Paris to advance generative models and LLM performance across research, engineering, and deployment efforts for product and frontier models.

You will lead independent research, design experiments, implement reusable code, evaluate results, and contribute to publications and open-source work, while mentoring teammates and coordinating with other teams.