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.
Core Responsibilities
- Practical understanding of vehicle dynamics and system identification
- Practical experience in generating test plans, collecting real-world data, and using real-world data for system identification of plant models for automatic control.
- 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
- 2-3 years of experience with experimental data collection and data analysis to estimate parameters of a plant model used for automatic control
- 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
Skills Required
- 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
- 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
- Experience deploying learned controllers on vehicles or mobile robots
- Familiarity with safety-constrained learning methods
- Background spanning both classical and modern control theory
What We Do
Teleo converts existing fleets of heavy equipment into semi-autonomous robots. Operators control machines from a remote desk, instantly switching between machines and across job sites.
