Staff Research Engineer, Robot Learning Systems

RoboForce; Milpitas (CA); On-site; USD 225,000 - 265,000; Full time

Benefits offered by this job

Stock options

Health insurance

Dental insurance

Vision insurance

401(k) plan

Visa sponsorship

Team lunches

Team dinners

Equity

Job summary

RoboForce is seeking a Staff Research Engineer, Robot Learning Systems in Milpitas, CA to lead the embodied systems stack across onboard AI, data collection, teleoperation, and on-robot reinforcement learning. You will own the end-to-end deployment on real robots and set the architectural direction for model execution and evaluation.

You will collaborate with robotics software and ML teams, mentor engineers, and help advance the company's data flywheel with real-world industrial robots.

Qualifications

• Bachelor's or Master's in CS, Robotics, EE, or related field, or PhD.
• Track record of leading complex robotic or embodied systems end-to-end.
• Proficiency in C++ and Python with real-time engineering skills.
• Hands-on experience with ROS/ROS2 and robot middleware.
• Experience deploying ML models into real-time robotic systems.
• Requires 5 days/week in-office collaboration.

Responsibilities

• Lead embodied systems powering RoboForce's data flywheel — onboard AI, data collection, teleoperation, and on-robot RL systems.
• Set the technical direction and architecture for how learned models run, evaluate, and improve on real robots.
• Deliver end-to-end systems on physical robots with reliable real-time operation.
• Own on-robot deployment and closed-loop evaluation of policies.
• Collaborate with robotics software and ML research teams to align interfaces and priorities.
• Mentor engineers and raise the technical bar for embodied systems work.

Skills

C++

Python

ROS/ROS2

Real-time systems

ML deployment

Leadership

Education

Bachelor's or Master's in CS/Robotics/EE

PhD considered

Tools

ROS middleware

Robot middleware

Job description

Staff Research Engineer, Robot Learning 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 Staff Research Engineer, Robot Learning Systemsto 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 $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, job‑related skills, experience, and relevant education or training, and may fall outside the listed range in exceptional cases.