At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver more of the critical infrastructure our society needs.
In August 2026, Bedrock-equipped excavators began doing fully autonomous, paid work on live commercial job sites. We've more than doubled our customer base since 2025, and our machines now work with contractors across the South, Midwest, and Mid-Atlantic
In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks.
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
Bedrock Robotics is an Equal Opportunity Employer
We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic.
Reasonable Accommodations
We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.