Research Scientist Intern — Agentic AI & Muse

Meta

Paris

Sur place

EUR 12 000 - 21 000

Plein temps

Il y a 9 jours

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Résumé du poste

Meta is seeking Research Interns to join the Meta Superintelligence Lab in one of the post-training modeling teams, focusing on Agentic AI, Muse and personal superintelligence. Internships run 12 to 24 weeks with flexible start dates, offering opportunities to develop novel algorithms and prototype features at scale.

Responsibilities include researching agentic LLMs, RL environments, and post-training techniques; collaborating with cross-functional teams; and disseminating findings through

Qualifications

  • Must have work authorization in the country of employment.
  • Experience with Python for ML projects.
  • Experience building systems based on ML/deep learning methods.
  • Preferred: grants, fellowships, patents, and first-authored publications at top venues.

Responsabilités

  • Develop novel state-of-the-art agentic AI algorithms and systems.
  • Research agentic LLMs, RL environments, post-training, and related topics.
  • Analyze and improve efficiency, scalability, and stability of agentic AI systems.
  • Advance science and technology of intelligent, agentic machines with reasoning and tool use.
  • Collaborate with researchers and cross-functional partners; communicate plans, progress and results.
  • Publish results and contribute to open source where applicable.

Connaissances

Python
Machine Learning
Deep Learning
Agentic AI

Formation

PhD

Outils

Open source libraries

Description du poste

Meta is seeking Research Interns to join the Meta Superintelligence Lab in one of the post-training modeling teams, focusing on Agentic AI, Muse and personal superintelligence. Internships run 12 to 24 weeks with flexible start dates, offering opportunities to develop novel algorithms and prototype features at scale.

Responsibilities include researching agentic LLMs, RL environments, and post-training techniques; collaborating with cross-functional teams; and disseminating findings through

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