D. degree in Computer Science, Computer Engineering, Artificial Intelligence, Robotics, Computer Vision, Machine Learning or related STEM field Experience with any of the following research areas: robotics, motion planning, embodied AI, human-robot interaction, sim-to-real transfer, learning from demonstration, reinforcement learning, dexterous manipulation, digital agents, vision language models, computer vision, egocentric perception, and/or LLMs Experience with machine learning models, and frameworks such as PyTorch, TensorFlow, etc Interpersonal experience: cross-group and cross-culture collaboration Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment Intent to return to degree program after the completion of the internship/co-op Hands-on experience working with real robot hardware, such as collecting data on, debugging, or deploying learned policies to physical robots, beyond simulation alone Proven track record of achieving significant results as demonstrated by accepted papers in top conferences or journals such as (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), Computer Vision (CVPR, ICCV, ECCV) 1+ years of experience in robotics or related field, or advanced degree with equivalent experience Experience designing and developing new robotic systems, platforms or infrastructure Experience with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources Experience solving complex problems and comparing alternative solutions, trade-offs, and diverse points of view to determine a path forward Experience working and communicating cross functionally in a team environment Experience building systems based on machine learning and/or deep learning methodsMeta builds technologies that help people connect, find communities, and grow businesses. Interns in this role are proficient in identified research areas such as artificial intelligence, machine learning, robotics, and embodied AI, particularly including areas such as transfer learning, learning from demonstration, reinforcement learning, action-conditioned world models, perception, representation learning, robot control, mobile manipulation, dexterous manipulation, and vision-language models.