Senior Autonomy Controls Engineer – Learning-Based Control

Teleo • Palo Alto, CA

20h • $180,000 - $230,000

About The Position

Teleo is a robotics startup disrupting a trillion-dollar industry. Teleo converts construction heavy equipment, like loaders, dozers, excavators, trucks, etc. into autonomous robots. This technology allows a single operator to efficiently control multiple machines simultaneously, delivering substantial benefits to our customers while significantly enhancing operator safety and comfort. Teleo is founded by Vinay Shet and Rom Clément, experienced technology executives who led the development of Lyft’s Self Driving Car and Google Street View. Teleo is backed by YCombinator, Up Partners, F-Prime Capital, and a host of industry luminaries. Teleo’s product is already deployed on several continents and generating revenue. Teleo is poised for rapid growth. This presents a unique opportunity to be part of a team that is creating a product with a profound impact on our customers, working on cutting-edge 100,000-pound autonomous robots, engineering intricate systems at the intersection of hardware, software, and AI, and joining the early stages of an exciting startup journey. 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.

Requirements

• 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

Nice To Haves

• 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

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed