Staff Research Engineer, Robot Learning Systems

Why RoboForce

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.

Requires 5 days/week in-office collaboration with the teams.

Bonus Qualifications

Experience with teleoperation and data-collection systems (e.g., VR, UR, GELLO, 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 (VLA, imitation learning, behavior cloning) and their real-time inference and control-integration characteristics.

Familiarity with manipulation stacks, whole-body control interfaces, or real-time middleware tuning.

Benefits

Visa sponsorship and green card support for qualified candidates.

Compensation: Salary $225,000–$265,000 USD + Bonus + Equity

The base salary range above represents the expected compensation for this full-time U.S. position. Final compensation will be determined based on role scope, level, location, job-related skills, experience, and relevant education or training, and may fall outside the listed range in exceptional cases.