Control Systems Software Engineer, Robotics

About the Role

We’re looking for a Robotics Control Systems Engineer to take on a foundational role within our robotics team. You’ll help architect, implement, tune, and verify the control infrastructure that enables intelligent, reliable, and responsive robot behavior.

This is a deeply hands-on role focused on real-time systems, actuation, dynamics, low-level hardware interaction, and whole-robot performance. You’ll spend significant time working directly with robots onsite: debugging behavior, tuning subsystems, running experiments, and providing feedback across mechanical, electrical, and software teams.

This role is based in San Francisco, CA, and requires in-person 5 days a week.

In this role, you will

• Design and implement real-time control algorithms for robotic systems, including motion control, feedback loops, state estimation, actuator control, and subsystem tuning.
• Define the control architecture from low-level actuators and hardware interfaces through whole-robot behavior and policy.
• Identify and characterize actuator, hardware, and software parameters through rigorous experimentation, testing, commissioning, and verification.
• Work with machine learning engineers to implement reinforcement learning models.
• Collaborate across mechanical, electrical, and software teams to integrate control logic with sensing and actuation hardware.
• Help inform the mechanical and electrical design to maximize capability and flexibility.
• Create the control system architecture; determine the correct level of abstraction from actuators all the way up to whole-robot policy.

You might thrive in this role if you

• Have a strong robotics background, including manipulators, mobile robots, or other complex physical systems.
• Have experience with actuation, control systems, robot dynamics, estimation, subsystem tuning, or whole-body motion.
• Are comfortable being hands-on with robotic hardware, including commissioning, debugging, testing, and iterating on real robots.
• Have strong fundamentals in classical control, system dynamics, and estimation, with exposure to modern learning-based or reinforcement learning approaches.
• Can move comfortably across the stack, from low-level actuator behavior and hardware interfaces to higher-level algorithms and whole-robot performance.
• Have experience writing performant production-quality software in C++ or Rust.
• Experience with robotic hardware and enjoy iterating quickly between simulation and real-world testing environments.