Robotics Software Engineer: Dexterous Manipulation & ML

Relari, inc.; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Relari, inc. is seeking robotics software engineers to build the software systems that enable robots to learn dexterous manipulation skills from human data.

You will work with dexterous hands and grippers across control, perception, teleoperation, simulation, and learned policy deployment, turning research ideas into working systems on physical robots. We welcome applicants who enjoy working on real hardware and applying learning-based approaches to robotics.

Qualifications

• Proficient in Python and/or C++ for robotics software.
• Experience developing software for physical robots, simulation, or related systems.
• Solid understanding of robotics fundamentals (kinematics, dynamics, planning, control, or state estimation).
• Ability to debug complex hardware-software systems independently.
• Experience with dexterous hands, contact-rich manipulation, or force/impedance control is a plus.
• Experience with computer vision, 3D perception, or pose estimation is a plus.
• Experience with ROS/ROS2, robot simulators, or robotics development frameworks is a plus.
• Experience with robot learning, imitation learning, reinforcement learning, or PyTorch is a plus.

Responsibilities

• Develop and maintain software for robot arms, dexterous hands, and grippers, including control, sensor integration, and real-time execution.

Skills

Python

C++

Robotics fundamentals

Debug complex systems

Dexterous hands experience

ROS/ROS2

PyTorch

Computer vision

Tools

ROS/ROS2

Robotics simulators

PyTorch

Camera calibration tools

Job description

Relari, inc. is seeking robotics software engineers to build the software systems that enable robots to learn dexterous manipulation skills from human data.

You will work with dexterous hands and grippers across control, perception, teleoperation, simulation, and learned policy deployment, turning research ideas into working systems on physical robots. We welcome applicants who enjoy working on real hardware and applying learning-based approaches to robotics.