D. degree in Computer Science, Artificial Intelligence, Robotics, or relevant technical field Understanding of the state of the art in learned dexterous manipulation and control, with judgment about which open problems are worth advancing Experience with Python and PyTorch or JAX, plus familiarity with ROS or comparable robotics middleware Must obtain work authorization in the 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 Robotics (RSS, ICRA, IROS, CoRL, T-RO, IJRR), Machine Learning (NeurIPS, ICML, ICLR, AAAI, JMLR), and Computer Vision (CVPR, ICCV, ECCV, TPAMI), or similar Experience training modern control policies, including flow matching, diffusion, imitation learning, reinforcement learning, and large behavior models Familiarity with modeling and analysis used in robotics including kinematics, dynamics, trajectory optimization, motion planning, perception, task planning, and control theory Hands-on experience with multi-fingered robotic hand manipulation, and with parallel-jaw or other grippers mounted on a robot arm Experience with motion retargeting across human and robot embodiments Experience with sim-to-real and real-to-sim workflows, including domain randomization, system identification, scene reconstruction, and closing the transfer gap Experience collecting and learning from real robot hand demonstrations, whether through teleoperation, kinesthetic guidance, or wearable capture, with bimanual setups a plus Experience with multimodal or self-supervised representation learning and cross-embodiment transfer Experience with vision based input recognition systems, such as hand tracking, pose estimation, and object pose estimation Experience working with time sequence sensor data, such as tactile and IMU streams Experience with hand force estimation and its applications in robot policy learning Experience designing data collection protocols and building high quality, diverse datasets Experience bringing up and debugging prototype software and hardware systems such as robot platforms, multi-camera tracking rigs, or wearable sensing systems Experience working and communicating cross functionally in a team environment Intent to return to the degree program after the completion of the internship/co-op Availability for minimum 16 consecutive week internshipMeta builds technologies that help people connect, find communities, and grow businesses. Dexterous Manipulation is the pillar attacking that problem directly, pairing a custom dexterous robotic hand with in-house wearable sensing, high accuracy simulation, and glove-driven teleoperation.