Senior Autonomy Controls Engineer – Learning-Based Control

Teleo
Palo Alto
Workplace: OnsiteFull timeFunction: Education & TrainingExperience: 2-3 yearsSkills: ["Safety-minded engineering","Problem-solving","Cross-functional collaboration","Debugging and validation"]

Own the transition from manually tuned MPC vehicle control to learning-based control policies that generalize across vehicles with minimal human intervention. Maintain safety and interpretability while building and validating learning-based approaches (imitation learning, reinforcement learning, and hybrid MPC + learning). Integrate learned controllers into the existing control stack, define interfaces between classical and learning components, and establish validation criteria before real-vehicle deployment.

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FursaFursa
Teleo
Teleo
5 months ago

Senior Autonomy Controls Engineer – Learning-Based Control

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Last checked: 22 hours agoStatus: Live

Job Summary

Own the transition from manually tuned MPC vehicle control to learning-based control policies that generalize across vehicles with minimal human intervention. Maintain safety and interpretability while building and validating learning-based approaches (imitation learning, reinforcement learning, and hybrid MPC + learning). Integrate learned controllers into the existing control stack, define interfaces between classical and learning components, and establish validation criteria before real-vehicle deployment.
Location: Palo Alto
Workplace: Onsite
Employment Type: Full time
Job Function: Education & Training
Seniority: Mid level

Key Responsibilities

  • •Own the move from MPC-based control to learning-driven control policies that adapt across vehicles with minimal human intervention.
  • •Design, implement, and iterate on learning-based control approaches (imitation learning, reinforcement learning, and hybrid MPC + learning).
  • •Integrate learned controllers into the existing vehicle control stack safely and incrementally.
  • •Define interfaces between classical control components (MPC, PID, state estimation) and learning-based components.
  • •Establish validation criteria for learned control policies prior to real-vehicle deployment and validate on physical platforms.

Key Requirements

  • •Own the transition from manually tuned MPC-based vehicle control to learning-driven control policies that adapt across vehicles while maintaining safety and interpretability.
  • •Practical understanding of vehicle dynamics and system identification.
  • •Experience generating test plans, collecting real-world data, and using that data for system identification of plant models for automatic control.
  • •Design and implement learning-based control approaches including imitation learning, reinforcement learning, and hybrid MPC + learning.
  • •Strong software engineering skills in C, C++, or Python and deep understanding of modern robotics control systems.
Experience:2-3 yearsRoboticsAutonomous vehiclesReal-world dataControl systemsReinforcement learningImitation learningModel-based RL
Skills:Safety-minded engineeringProblem-solvingCross-functional collaborationDebugging and validation
Tech Stack:CC++PythonMPCPIDReinforcement learningImitation learningHybrid MPCReinforcement learning for controlModel-based RL

Company Brief

Teleo
Develops AI-driven conversational agents and voice assistants to automate customer interactions, sales outreach, and support workflows across phone and digital channels, aiming to improve efficiency and engagement for businesses.
Industry: AI & Machine Learning
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