Postdoctoral Researcher (PhD) - FAIR - Core Learning

Meta; Paris; Sur place; EUR 42 000 - 66 000; Plein temps

Résumé du poste

Meta's Fundamental AI Research (FAIR) in Paris is seeking a postdoctoral researcher to advance generative models, with a focus on data efficiency and continual learning in large language models.

The role spans research, engineering, and deployment for both product and frontier model efforts, offering opportunities to publish, mentor others, and collaborate across cross-functional teams.

Qualifications

• PhD in CS/Math or related quantitative field.
• Experience training, fine-tuning, and experimenting on foundation models beyond black-box usage.
• Must be able to obtain and maintain work authorization in France.

Responsabilités

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

Connaissances

Research leadership

Generative models

LLMs

Systematic experimentation

Cross-functional collaboration

Formation

PhD in CS/Math

Outils

PyTorch

Description du poste

Summary:

Meta's Fundamental AI Research (FAIR) organization is seeking a postdoctoral researcher 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.

Required Skills:

Postdoctoral Researcher (PhD) - FAIR - Core Learning Responsibilities:

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

Minimum Qualifications:

Minimum Qualifications:

1. Currently possess or be pursuing a PhD in Computer Science, Mathematics, or a similar quantitative discipline
2. Demonstrated experience with training, fine-tuning, and experimentation on foundation models beyond black-box usage
3. Must be able to obtain and maintain work authorization in the country of employment

Preferred Qualifications:

Preferred Qualifications:

1. Ability to communicate complex ideas with peers
2. Hold first-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR)
3. Familiarity with PyTorch

Industry:

Internet