Position OverviewWe're looking for a Controls Engineer to design and optimize trajectory generation and control systems for an autonomous truck. You'll own safety-critical systems and ensure reliable execution of complex maneuvers, working closely with the ML team to integrate real-time path outputs while enforcing system constraints and safety checks. This role spans embedded systems and diverse compute platforms, and offers a rare opportunity to connect cutting-edge ML with production autonomy on a small, high-ownership team.
Key Responsibilities- Design, implement, tune, and deploy real-time controllers for autonomous trucks, taking ownership from modeling through on-vehicle validation
- Develop and maintain vehicle dynamics models and perform system identification to support controller design and simulation fidelity
- Build and improve estimation and sensor fusion pipelines for vehicle state (Kalman filters, EKF/UKF, etc.)
- Validate controllers through SIL/HIL testing, closed-loop simulation, and structured on-vehicle experiments
- Debug, analyze, and iterate on controllers in the field using vehicle logs and telemetry
- Collaborate with teams across ML autonomy, system software, hardware, and safety on interfaces, requirements, and integration
- Contribute to the controls codebase in Rust with a focus on safety, reliability, real-time performance, and maintainability
- Document design decisions, experiments, and tuning methodology clearly for the broader team
Minimum Qualifications- BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field-or equivalent industry experience
- Strong foundation in classical control theory: PID, LQR, state-space methods
- Industry experience developing real-time control systems deployed on physical hardware
- Strong proficiency in Rust and/or C++ for performance-critical systems
- Experience with estimation techniques (Kalman filters, complementary filters, or similar)
- Demonstrated ability to debug and tune controllers on real hardware
- Experience with ROS/ROS2/Autoware/Iceoryx or comparable robotics middleware
- Strong written and verbal technical communication
- Eligible to work in the United States
Preferred Qualifications- Background in nonlinear, robust, or adaptive control
- Experience with Model Predictive Control (MPC) and optimization tooling (QP solvers, CasADi, Acado, etc.)
- Experience with Bazel or similar build systems for complex codebases
- Working knowledge of vehicle dynamics like tire models, lateral/longitudinal dynamics, and load transfer
- Comfort operating as an early team member-high ownership, low ego, fast iteration
CompensationThis role is eligible for base salary \+ benefits \+ equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location.
Additional InformationAs part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact **[email protected]** to exercise this right or if you have further questions on the use of AI tools in our hiring process.
