Staff Research Engineer, Robot Learning Systems

RoboForce•Milpitas, CA

1d•$225,000 - $265,000•Onsite

About The Position

RoboForce is an AI robotics company developing Physical AI-powered Robo-Labor for dull, dirty, and dangerous work. Its robots are designed for demanding industrial environments, with an emphasis on real-world deployment and scalability. The Staff Research Engineer, Robot Learning Systems will lead and own the engineering of systems that turn embodied AI into real-world robot behavior. This role is the technical owner and lead for the full embodied systems stack, including the onboard AI system, data collection system, teleoperation systems, and on-robot reinforcement learning system. The position sets technical direction hands-on and connects data, models, and physical robot actions. The role requires five days per week of in-office collaboration with the teams. It is a full-time U.S. position, with a stated base salary range of $225,000–$265,000 USD, plus bonus and equity. Final compensation depends on factors including role scope, level, location, skills, experience, and education or training, and may fall outside the range in exceptional cases.

Requirements

Bachelor's or master's degree in Computer Science, Robotics, Electrical Engineering, or a 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 or 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 or policies into real-time robotic or autonomous systems, with emphasis on system ownership and building rather than model training or research.
Availability for five days per week of in-office collaboration with the teams.

Nice To Haves

Experience with teleoperation and data-collection systems, such as VR, UR, GELLO, or UMI, and the challenges of collecting high-quality robot data at scale.
Experience with on-robot reinforcement learning or closed-loop policy-improvement systems.
Familiarity with robot learning policies such as VLA, imitation learning, and behavior cloning, including their real-time inference and control-integration characteristics.
Familiarity with manipulation stacks, whole-body control interfaces, or real-time middleware tuning.

Responsibilities

Lead and own the engineering of the embodied systems powering RoboForce's data flywheel: onboard AI/inference, data collection, teleoperation, and on-robot reinforcement learning systems.
Set technical direction and architecture for how learned models run on robots, are evaluated, and improve.
Deliver 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, translating real-world performance into measurable improvements.
Partner with and influence robotics software and ML research teams to align interfaces and priorities across the stack.
Mentor engineers and raise the technical bar for embodied systems work.