Lead AI Research Engineer, Embodied Systems

Milpitas, CA

Why RoboForce

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.

We are looking for a Lead AI Research Engineer, Embodied 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.

Competitive stock options/equity programs.

Health, dental, and vision insurance, 401(k) plan.

Visa sponsorship and green card support for qualified candidates.

Lunches and dinners, a fully stocked kitchen, and regular team‑building events.

Compensation: Salary $190,000–$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.