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San Francisco, California, United States
Onsite
At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects.
We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction.
This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.
We are building our first fleet of autonomous construction machines and are seeking a Controls and Robot Learning Engineer. In this role, you will contribute to the development of crucial components of our onboard and offboard autonomy system. You will be responsible for creating models to be used for onboard controls, as well as analyzing, evaluating and simulating the system dynamics of complex, 100,000-pound construction robots.
Onboard Control: Develop control laws for the base vehicle and automated arms, utilizing techniques such as MPC, Reinforcement Learning, linear and non linear control, computed torque, vehicle dynamics, and impedance control.
System Identification and Modeling: Build models that capture the state and control input propagation of complex construction robots like excavators. This involves a deep understanding of the direct and inverse geometry of robot arms (4 to 7 DOFs), vehicle dynamics, and overall system calibration.
5+ years of professional engineering or research experience in control and real-time embedded systems
MSc or PhD in Computer Science or Robotics
Deep understanding of reinforcement learning, imitation learning, and optimization for dynamic systems
Strong programming skills (C++/Rust, Python)
Strong data analysis skills
Experience with safety-critical systems
Experience with machine learning training pipelines, especially reinforcement learning (RL) using learned or simulated plant models
Practical application of RL or model predictive control (MPC) for control algorithms in production autonomy environments
Experience working with pose estimation systems
Experience with controlling and modeling hydraulic systems
Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.