AI Research Scientist, Reinforcement Learning

Meta Meta · Big Tech · New York, NY

Meta's Fundamental AI Research lab is seeking a Research Scientist to advance physical AI capabilities, focusing on novel post-training paradigms for LLMs using reinforcement learning and integrating large-scale simulation. The role involves research engineering, data manipulation, and simulator integration for applications in robotic hardware, mobile vehicles, and semiconductors.

What you'd actually do

  1. Explore and develop novel post-training paradigms for LLMs using reinforcement learning
  2. Explore and develop novel LLM post-training recipes using 3D data
  3. Integrate large-scale simulation into LLM post-training
  4. Explore mechanical, aerospace, civil, and other engineering disciplines and how to enable LLMs to solve key problems in these domains

Skills

Required

  • PhD degree in Artificial Intelligence, Computer Vision (3D), Physical AI, Machine Learning, relevant technical field, or equivalent practical experience
  • Research experience in reinforcement learning, representation learning, self-supervised learning, multimodal learning, robotics policy development, computer vision (3D), egocentric perception, embodied AI and/or LLMs, control theory, optimization algorithms
  • Experience in C/C++ and Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, AAAI, JMLR and Computer Vision (CVPR, ICCV, ECCV, TPAMI)
  • knowledge of post-training for LLMs using reinforcement learning techniques
  • computer vision expertise in 3D
  • Experience integrating and debugging prototype/scientific software-hardware systems including mechanical, aerospace, or civil engineering domain-specific simulation
  • Experience working and communicating cross-functionally in a team environment

Nice to have

  • Experience in the fields of mechanical, aerospace, civil engineering or other engineering domains

What the JD emphasized

  • PhD degree
  • first-authored publications at leading workshops or conferences

Other signals

  • novel post-training paradigms for LLMs using reinforcement learning
  • large-scale data manipulation and simulator integration
  • physical AI capabilities
  • robotic hardware, mobile vehicles, and novel semiconductors