Controls & Reinforcement Learning Engineer at Foundation Robotics Labs Inc. – San Francisco, California

Foundation is developing the future of general purpose robotics with the goal to address the labor shortage.

Our mission is to create advanced robots that can operate in complex environments, reducing human risk in conflict zones and enhancing efficiency in labor-intensive industries.

We are on the lookout for extraordinary engineers and scientists to join our team. Your previous experience in robotics isn't a prerequisite — it's your talent and determination that truly count.

We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work.

All positions are based in San Francisco.

Our Culture

We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture here https://foundation.bot/culture.

Who should join:

• You like working in person with a team in San Francisco.
• You deeply believe that this is the most important mission for humanity and needs to happen yesterday.
• You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well.
• You care about aesthetics and design inside out. If it's not the best product ever, it bothers you, and you need to “fix” it.
• You don't need someone to motivate you; you get things done.

About the Role

We're building a high-degree-of-freedom tendon-driven robotic hand, and we're looking for the first software engineer dedicated to it. This is a rare opportunity to own the entire controls and learning stack for one of the hardest manipulation problems in robotics — from low-level motor control to grasp planning — with direct impact on the product from day one.

You'll work with a small team and have significant autonomy in how you approach problems. We're not looking for someone who needs a roadmap handed to them; we're looking for someone who can build one.

What You'll Own

• Low-level motor control for a tendon-driven, high-DOF hand system, including tension management, coupled joint dynamics, and real-time feedback loops
• Higher-level coordination across actuators to achieve stable, dexterous finger and wrist trajectories
• Integration of tactile, proprioceptive, and other sensor modalities into the control architecture
• End-to-end RL pipeline for grasp planning and manipulation — from simulation training through sim2real transfer and physical deployment
• Tooling, testing infrastructure, and software architecture decisions for the hand software stack

What We're Looking For

• Strong foundation in both classical control theory and modern RL — you're comfortable reasoning about stability and dynamics as well as policy optimization
• Hands-on experience deploying learned policies on physical hardware, not just in simulation
• Proficiency in Python and C++; familiarity with ROS/ROS2 and real-time systems
• Experience with physics simulators (MuJoCo, Isaac Lab, or similar) and deep learning frameworks (PyTorch, JAX)
• High agency and comfort operating independently in an early-stage environment — you define the problem as much as you solve it

Bonus Points

• Prior experience with tendon-driven or cable-actuated systems
• Experience with dexterous manipulation, multi-fingered hands, or compliant mechanism control
• Familiarity with tactile sensing integration
• Contributions to open-source robotics or RL projects
• M.Sc. or Ph.D. in Robotics, Controls, Computer Science, or a related field

Benefits

We provide market standard benefits (health, vision, dental, 401k, etc.). Join us for the culture and the mission, not for the benefits.

Salary

The annual compensation is expected to be between $150,000 - $280,000. Exact compensation may vary based on skills, experience, and location.