Currently has or is in the process of obtaining a PhD degree in Artificial Intelligence, Physical AI, Computer Vision (3D), Machine Learning, Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, relevant technical field, or equivalent practical experience Research and/or work experience in at least one of the following research areas: physical AI, simulation, 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 the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience integrating and debugging prototype/scientific software-hardware systems including mechanical, aerospace, or civil engineering domain-specific simulation 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) Prior research or work experience in the fields of mechanical, aerospace, civil engineering or other engineering domains Experience advancing AI, including contributions to open source libraries and frameworks Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources Experience in utilizing theoretical and empirical research to solve problems Experience working and communicating cross functionally in a team environment Intent to return to degree-program after the completion of the internship/co-opMeta builds technologies that help people connect, find communities, and grow businesses. We're especially interested in candidates with expertise across physical engineering (mechanical, electrical and aerospace engineering) design, simulation, reinforcement learning, and LLM post-training, including: Novel post-training paradigms for LLMs using reinforcement learning LLM post-training recipes using 3D data Integration of large-scale simulation into LLM post-training Computer vision (3D), representation learning, and self-supervised learning Modeling for the physical world across mechanical, aerospace, civil, and other engineering domains.