Research Scientist Intern, Dexterous Manipulation and Contact-Rich Control (PhD)

Meta; Redmond; Presencial; AUD 78.000 - 118.000; Jornada completa

Descripción de la vacante

Agios, a research arm within Reality Labs, invites applications for a Research Scientist Intern. You will advance learned feedback control for multi-fingered robotic hands, focusing on reinforcement learning in realistic simulation and sim-to-real transfer to real hardware.

Internships run 12–24 weeks with multiple start dates. Candidates should have a PhD in a relevant field and evidence of impactful research, including publications or open-source work, and familiarity with Python and physics

Formación

• PhD in Computer Science, Robotics, Mechanical or Electrical Engineering, or relevant field.
• Research in reinforcement learning or learning-based control for manipulation or locomotion.
• Experience with Python and physics simulators such as MuJoCo, Isaac, or Drake.

Responsabilidades

• Work on fast, stable learned feedback control for multi-fingered robotic hands.
• Advance reinforcement learning in high-fidelity simulation and transfer to real robots.
• Collaborate with hardware, simulation, controls, and policy teams on projects.
• Contribute to publications, open-source code, or significant project results.

Conocimientos

Reinforcement learning

Learning-based control

Python

Sim-to-real transfer

Multi-fingered hands

Cross-functional communication

Educación

PhD in Computer Science, Robotics, Mechanical or Electrical Engineering

Herramientas

MuJoCo

Isaac

Drake

Descripción del empleo

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 on fast, stable, learned feedback control for multi-fingered robotic hands. Your focus will be reinforcement learning in high-fidelity simulation, tactile and proprioceptive feedback, and transfer to real robot hands, with mentors who span hardware, simulation, controls, and policy learning.

Our internships are sixteen (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, Robotics, Mechanical or Electrical Engineering, or relevant technical field
• Research experience in reinforcement learning or learning-based control for robotic manipulation or locomotion, demonstrated through publications, open-source code, or substantial projects
• Experience with Python, and with a physics simulator such as MuJoCo, Isaac, or Drake
• 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 RSS, CoRL, ICRA, IROS, T-RO, IJRR, NeurIPS, ICML, or ICLR
• Experience with sim-to-real transfer of contact-rich, locomotion, or dexterous manipulation policies, including domain randomization, system identification, and teacher–student distillation
• Experience using tactile sensing, force/torque sensing, or contact estimation in policy learning or control
• Experience in retargeting for locomotion or manipulation
• Grounding in manipulation mechanics and control, such as contact modeling, impedance and admittance control, and trajectory optimization
• Hands-on experience with multi-fingered robotic hands or grippers on real hardware
• Experience working and communicating cross functionally in a team environment
• Availability for minimum 16 consecutive week internship