Lead the transition from hand-tuned MPC to learning-driven control policies, design and integrate learning-based controllers, define interfaces with classical control, validate policies, and ensure safe real-vehicle deployment.
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.
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.
Top Skills
C
C++
Hybrid Mpc
Imitation Learning
Model Predictive Control (Mpc)
Model-Based Rl
Pid
Policy Optimization
Python
Real-Time Systems
Reinforcement Learning
Residual Learning
State Estimation
Teleo Palo Alto, California, USA Office
Palo Alto, Palo Alto, CA, United States, 94306
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