Why RoboForce

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

We are looking for a

Staff Research Engineer, Robot Learning Systems

to lead and own the engineering of the systems that turn embodied AI into real-world robot behavior. You will be the technical owner and lead for the full embodied systems stack — the onboard AI system, the data collection system, the teleoperation systems, and the on-robot reinforcement learning system — driving the direction hands-on and closing the loop between data, models, and action in the physical world.

Responsibilities

• Lead and own, from the engineering side, the embodied systems that power RoboForce's data flywheel — the onboard AI (inference) system, the data collection system, the teleoperation systems and the on-robot reinforcement learning system.
• Set the technical direction and architecture for how learned models run, are evaluated, and improve on real robots.
• Deliver these systems end-to-end on physical robots — from bring-up through reliable, real-time operation in demanding industrial environments.
• Own on-robot deployment and closed-loop evaluation of policies, turning real-world performance into measurable improvements.
• Partner with and influence the robotics software team and the ML research team to align interfaces and priorities across the stack.
• Grow the direction — mentor engineers and raise the technical bar for embodied systems work.

Requirements

• Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or related field with significant relevant experience, or a PhD degree.
• Track record of leading complex robotic or embodied systems end-to-end and setting technical direction for other engineers.
• Strong proficiency in both C++ and Python, with solid systems programming and real-time / performance-critical engineering skills.
• Hands-on experience with ROS/ROS2 and robot middleware, including real-time integration of sensing, control, and compute.
• Experience integrating and deploying ML models/policies into real-time robotic or autonomous systems — system ownership and building, rather than model training or research.