Job Description

Teleo, a Havoc company, is a robotics company that transforms construction heavy equipment, including loaders, dozers, excavators, and trucks, into autonomous robots for commercial and defense applications. Our technology enables a single operator to supervise and control multiple machines simultaneously, delivering significant productivity gains while improving operator safety and comfort.

Teleo was founded by a team of experienced technology leaders who previously led the development of Lyft's Self-Driving Car program and Google Street View. Teleo recently announced its merger with Havoc AI, a fast-growing defense technology company developing coordinated fleets of autonomous maritime vessels.

This is a unique opportunity to join a team building technology with real-world impact. You will work on cutting-edge 100,000-pound autonomous robots and engineer complex systems at the intersection of hardware, software, robotics, and AI.

About the Role
Own the transition from manually tuned MPC-based vehicle control to learning-driven control policies that adapt across vehicles with minimal human intervention, while maintaining safety and interpretability.

Core Responsibilities

• Design and implement learning-based control approaches (imitation learning, reinforcement learning, hybrid MPC + learning)
• Reduce dependence on hand-tuned control parameters through data-driven methods
• Integrate learned controllers into the existing vehicle control stack safely and incrementally
• Define interfaces between classical control (MPC, PID, state estimation) and learning-based components
• Work closely with the Principal Controls Engineer to translate classical control insights into learning-friendly formulations
• Establish validation criteria for learned control policies before real-vehicle deployment

Required Qualifications

• Strong software engineering skills in C, C++, or Python (production-quality code)
• Deep understanding of modern robotics control systems
• Experience with learning-based control or policy optimization for real-world systems
• Comfort working close to hardware and real-time constraints

Preferred Qualification

• Reinforcement learning or imitation learning for control
• Model-based RL, residual learning, or hybrid MPC architectures
• Control under uncertainty and partial observability
• Debugging and validating control systems on physical platforms

Bonus Points

• Experience deploying learned controllers on vehicles or mobile robots
• Familiarity with safety-constrained learning methods
• Background spanning both classical and modern control theory

Teleo is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. All qualified people are encouraged to apply.