Position Overview
We’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
Compensation
This 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 Information
As 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.
Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.
