AI Research Scientist, Reinforcement Learning

Meta's Fundamental AI Research lab is seeking a Research Scientist to drive foundational research aimed at advancing physical AI capabilities. We seek to generate advanced engineer designs such as robotic hardware, mobile vehicles, and novel semiconductors. This role will involve heavy software research engineering, such large-scale data manipulation and simulator integration.

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

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Currently has or is in the process of obtaining a PhD degree in Artificial Intelligence, Computer Vision (3D), Physical AI, Machine Learning, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Research experience in at least one of the following research areas: 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) Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment 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) This position will require knowledge of post-training for LLMs using reinforcement learning techniques. It will also involve novel modalities such as 3D and engineering domain-specific simulators, so computer vision expertise in 3D is welcome as well 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 Prior work experience in the fields of mechanical, aerospace, civil engineering or other engineering domains