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# Staff Research Engineer, Robot Learning Systems

- **Company:** Roboforce Inc.
- **Location:** United States
- **Salary:** $225,000.0 - $265,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Middleware, Python (Programming Language), Machine Learning, System Programming, Reinforcement Learning, Information Technology, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://startup.jobs/staff-research-engineer-robot-learning-systems-roboforce-10696796

## About the Role

* 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.

## Description

* 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.