Humanoid Robotics Researcher & Systems Engineer

Reward AI; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Reward AI is building frontier intelligence for robots, connecting learning, control, and hardware to enable capable, reliable behavior across varied bodies and tasks. The role spans research and implementation, offering exposure to simulation and real hardware work, from initial experiments to repeatable robotic behaviors in dynamic environments.

Join a multidisciplinary team tackling locomotion, dexterous manipulation, and real-time control, with opportunities to experiment in both simulation

Qualifications

• Background in robotics, controls, ML, or mechanical engineering through research or practical work.
• Experience in robot learning, dynamics, motion planning, or state estimation.
• Ability to develop and debug robotics software in Python or C++, with sim or DL tools.
• Hands-on work with humanoids, legged robots, or complex robotic systems.
• Proven ability to build and evaluate working robotic systems and solve cross-domain problems.

Responsibilities

• Develop methods for whole-body control, locomotion, dexterous manipulation, and coordinated movement.
• Build training and evaluation workflows using simulation, human motion, and data sources.
• Investigate transfer from simulation to hardware and robustness to changes in contact, objects, terrain, dynamics.
• Integrate learned behaviors with perception, state estimation, planning, and real-time control systems.
• Run hardware experiments with safeguards, diagnose failures, improve robot capability and reliability.

Skills

Robotics background

Controls

Machine learning

Mechanical engineering

Research experience

Python

C++

Simulation tools

Deep learning tools

Tools

Python

C++

Simulation

Deep learning

Job description

Reward AI is building frontier intelligence for robots, connecting learning, control, and hardware to enable capable, reliable behavior across varied bodies and tasks. The role spans research and implementation, offering exposure to simulation and real hardware work, from initial experiments to repeatable robotic behaviors in dynamic environments.

Join a multidisciplinary team tackling locomotion, dexterous manipulation, and real-time control, with opportunities to experiment in both simulation