Bedrock Robotics
Controls and Robot Learning Engineer
About this role
Bedrock is seeking a Controls and Robot Learning Engineer to develop autonomous systems for construction equipment. You'll design control algorithms and build dynamic models for 100,000-pound excavators and similar machines, combining classical control theory with reinforcement learning to operate in real-world job sites.
What you'll do
- Develop control laws for vehicle base and robotic arms using MPC, reinforcement learning, and classical control techniques
- Build and calibrate system identification models for complex construction robots with multiple degrees of freedom
- Design and implement onboard control systems for autonomous excavators and heavy equipment
- Analyze and simulate system dynamics of large construction machines
- Create models for onboard and offboard autonomy system components
- Evaluate control performance in production autonomy environments
What they're looking for
- Model Predictive Control (MPC) and reinforcement learning
- Kinematics and dynamics (forward/inverse geometry, vehicle dynamics)
- C++, Rust, and Python programming
- Real-time embedded systems
- Data analysis and system identification
- Safety-critical systems design
- Computed torque and impedance control
- Machine learning training pipelines
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Bedrock Robotics
Bedrock Robotics develops autonomous construction machinery powered by AI and robotics technology. The company is hiring for roles spanning developer infrastructure, simulation systems, hardware engineering, field robotics application, and frontend engineering to support the development and deployment of autonomous excavators and heavy equipment.
View all jobs at Bedrock RoboticsLikely interview questions
- Describe your experience designing control systems for real-world robotics applications—what were the main challenges moving from simulation to production?
- How have you applied reinforcement learning or MPC to control dynamic systems, and what metrics did you use to validate performance?