Control Systems Software Engineer, Robotics
Architects, implements, and tunes real-time control systems for OpenAI's robots — motion control, feedback loops, state estimation, and actuator control — working hands-on with physical hardware onsite in San Francisco five days a week. Core stack is C++/Rust for production control software, classical control and dynamics, plus collaboration with ML engineers on reinforcement learning models.
- Compensation
- $380K–$460K / yr
- Location
- San Francisco
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Experience writing performant production-quality software in C++ or Rust
Nice to have
- Strong robotics background, including manipulators, mobile robots, or other complex physical systems
- Experience with actuation, control systems, robot dynamics, estimation, subsystem tuning, or whole-body motion
- Comfortable being hands-on with robotic hardware, including commissioning, debugging, testing, and iterating on real robots
- Strong fundamentals in classical control, system dynamics, and estimation, with exposure to modern learning-based or reinforcement learning approaches
- Ability to move comfortably across the stack, from low-level actuator behavior and hardware interfaces to higher-level algorithms and whole-robot performance
- Experience with robotic hardware and iterating quickly between simulation and real-world testing environments
Full description
About the Team
Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives.
**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.