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Staff Research Engineer, Robot Learning Systems

roboforce • United States
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Lead engineering of embodied AI systems for RoboForce’s robots, focusing on real-world deployment and scalability. Own the full systems stack—onboard AI, data collection, teleoperation, and reinforcement learning—while driving technical direction and mentoring engineers. Requires expertise in C++, Python, ROS/ROS2, and real-time robotic systems with a track record in end-to-end robotic system ownership.

Key Highlights
Lead full embodied AI systems stack for RoboForce’s industrial robots
Own end-to-end deployment of onboard AI, data collection, teleoperation, and reinforcement learning systems
Mentor engineers and set technical direction for real-time robotic systems
Key Responsibilities
Lead and own the full embodied AI systems stack (onboard AI, data collection, teleoperation, reinforcement learning)
Set technical direction and architecture for real-time robotic systems deployment
Deliver end-to-end systems on physical robots in industrial environments
Own on-robot deployment and closed-loop evaluation of policies
Partner with robotics software and ML research teams to align priorities
Mentor engineers and raise technical standards
Technical Skills Required
C++ Python ROS/ROS2
Benefits & Perks
Competitive stock options/equity programs
Health, dental, and vision insurance
401(k) plan
Visa sponsorship and green card support
Lunches, dinners, and team-building events
Nice to Have
Experience with teleoperation/data-collection systems (e.g., VR, UR, GELLO, UMI)
Experience with on-robot reinforcement learning or closed-loop policy improvement
Familiarity with robot learning policies (VLA, imitation learning, behavior cloning)
Familiarity with manipulation stacks, whole-body control, or real-time middleware tuning

Job Description

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.

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