Research Scientist Intern - Physical Engineering AI

Meta's Fundamental AI Research (FAIR) lab is seeking Research Interns to join its team, which drives foundational research aimed at advancing physical engineering AI capabilities. The goal is to generate advanced engineering designs such as robotic hardware, vehicles, and novel semiconductors. This internship involves heavy software research engineering, including large-scale data manipulation and simulator integration. Interns have the opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Areas of interest include physical engineering design and 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 domainsThe team offers internships lasting twelve to twenty-four weeks, with various start dates throughout the year. ## Responsibilities - 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. - Brainstorm with research mentors, review literature and existing solutions to a challenging real-world research problem. - Develop novel solutions, implement prototypes, and perform extensive experiments to test proposed solutions in meaningful benchmarks and metrics, analyze the results, and verify conclusions. - Draft and polish research reports and/or publications. - Present research outcomes to internal and/or external audiences. - Contribute research that can be applied to Meta product development. ## Minimum Qualifications - 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, a relevant technical field, or equivalent practical experience. - Research and/or work experience in at least one of the following 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, or optimization algorithms. - Experience in C/C++ and Python and deep learning frameworks such as PyTorch or TensorFlow. - Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment. ## Preferred Qualifications - Experience integrating and debugging prototype/scientific software-hardware systems, including mechanical, aerospace, or civil engineering domain-specific simulation. - Proven track record of significant results, demonstrated by grants, fellowships, patents, or first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, AAAI, JMLR, CVPR, ICCV, ECCV, or TPAMI. - Prior research or work experience in 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 using theoretical and empirical research to solve problems. - Experience working and communicating cross-functionally in a team environment. - Intent to return to a degree program after completion of the internship/co-op.