Senior Autonomy Controls Engineer – Learning-Based Control at Teleo — Palo Alto, USA | Base Career

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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
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

Senior Autonomy Controls Engineer – Learning-Based Control