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.
What You'll Do- 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.
What We're Looking For- 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
Ways to Stand Out from the Crowd- 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.
