研究科学家实习生，机器人基础模型

Meta（Meta Platforms）岗位：Research Scientist Intern, Robot Foundation Models (PhD) Responsibilities

Drive an independent research thread on learned control and motion transfer for dexterous hands, from problem formulation through real robot or high fidelity simulation evaluation.

Build training pipelines for modern policy classes, including flow matching and diffusion policies, imitation learning, reinforcement learning, large behavior models, and VLAs.

Develop representations that fuse multimodal human and robot data, including video, hand pose, tactile and contact force, and IMU, into a shared action space.

Construct data collection pipelines and benchmarks for dexterous manipulation, including held-out and out-of-distribution protocols that test generalization across objects, motions, and embodiments.

Collaborate across an interdisciplinary team of hardware, simulation, controls, and policy researchers, and communicate research plans, progress, and results.

Currently has or is in the process of obtaining a Ph.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 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 bringing up and debugging prototype software and hardware systems such as robot platforms, multi-camera tracking rigs, or wearable sensing systems

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Currently has or is in the process of obtaining a Ph.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.