Senior Autonomy Controls Engineer - Learning-Based Control

Teleo Job OpportunityTeleo, 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 RoleOwn 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 ResponsibilitiesPractical understanding of vehicle dynamics and system identificationPractical 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 methodsIntegrate learned controllers into the existing vehicle control stack safely and incrementallyDefine interfaces between classical control (MPC, PID, state estimation) and learning-based componentsWork closely with the Principal Controls Engineer to translate classical control insights into learning-friendly formulationsEstablish validation criteria for learned control policies before real-vehicle deploymentRequired Qualifications2-3 years of experience with experimental data collection and data analysis to estimate parameters of a plant model used for automatic controlStrong software engineering skills in C, C++, or Python (production-quality code)Deep understanding of modern robotics control systemsExperience with learning-based control or policy optimization for real-world systemsComfort working close to hardware and real-time constraintsPreferred QualificationReinforcement learning or imitation learning for controlModel-based RL, residual learning, or hybrid MPC architecturesControl under uncertainty and partial observabilityDebugging and validating control systems on physical platformsBonus PointsExperience deploying learned controllers on vehicles or mobile robotsFamiliarity with safety-constrained learning methodsBackground spanning both classical and modern control theoryTeleo 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.