Staff Research Engineer, Robot Learning Systems

RoboForce•Milpitas, CA

1d•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 a focus on real-world deployment and scalability. The Staff Research Engineer, Robot Learning Systems will lead and own the engineering of the systems that turn embodied AI into real-world robot behavior. This person will provide technical leadership for the full embodied systems stack, including onboard AI, data collection, teleoperation, and on-robot reinforcement learning. The role is hands-on and focuses on connecting data, models, and actions on physical robots, delivering reliable real-time systems in industrial environments, and improving policies through closed-loop evaluation. The position requires five days per week of in-office collaboration.

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 or ROS 2 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 a focus on system ownership and building rather than model training or research.
Ability to collaborate with teams in-office five days per week.

Nice To Haves

Experience with teleoperation and data-collection systems, such as VR, UR, GELLO, or UMI, and with 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, or 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 that power RoboForce’s data flywheel: onboard AI inference, data collection, teleoperation, and on-robot reinforcement learning.
Set the technical direction and architecture for how learned models run, are evaluated, and improve on real robots.
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 policy evaluation, turning real-world performance into measurable improvements.
Partner with and influence the 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.