Research Scientist Intern, Robot Foundation Models (PhD)

Meta; Redmond; On-site; AUD 50,000 - 79,000; Full time

Job summary

Agios is a full stack research organization within Reality Labs Research, focusing on dexterous manipulation, learning-based control, and human-to-robot transfer. This internship will explore learned models that transform human demonstrations into robot behaviors, including retargeting and teleoperation.

Interns will work with mentors across hardware, simulation, controls, and policy learning, contributing to research that advances robot manipulation and real-world teleoperation capabilities

Qualifications

• Ph.D. degree in Computer Science, AI, Robotics, or related technical field.
• Strong background in learned dexterous manipulation and control.

Responsibilities

• Develop learned models that convert human demonstration data into executable robot behavior (retargeting, manipulation policies, teleoperation).
• Collaborate with hardware, simulation, controls, and policy-learning mentors.
• Publishments of significant results via grants, fellowships, patents or top-tier conferences.

Skills

Python

PyTorch

JAX

ROS

Reinforcement Learning

Imitation Learning

Control Theory

Trajectory Optimization

Education

Ph.D. in Computer Science / AI / Robotics

Tools

ROS

Job description

Agios is a full stack research organization within Reality Labs Research, spanning materials and manufacturing, sensors and actuators, simulation, and learned control. Our mission is to build systems that enhance human capabilities through intelligent machine collaboration. Today that work centers on what we call the last bottleneck: AI has compressed the digital side of research and development, while the physical side still moves at human speed. 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.

As a Research Scientist Intern, you will work at the intersection of robot learning, dexterous control, and human-to-robot transfer, with mentors who span hardware, simulation, controls, and policy learning. Your focus will be learned models that turn human demonstration data into executable robot behavior, covering retargeting, manipulation policies, and teleoperation.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

• 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), 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 internship