Principal Machine Learning Integration Engineer

We are seeking a passionate and experienced engineer to help bring machine learning–based motion planning to the car. In this role, you will focus on deploying, optimizing, and maintaining ML-driven planning and control algorithms for real-time autonomous driving. You will collaborate across motion planning, controls, and software engineering teams to ensure models run reliably in production, under strict performance and safety constraints.

If you are excited by the challenge of bridging advanced ML with safety-critical, resource-constrained vehicle platforms—and want to shape the future of autonomy—we encourage you to apply.

What You’ll Be Doing

• Deploy ML-based motion planning and control models onto vehicle platforms, ensuring performance under resource constraints.
• Optimize models for inference speed, latency, and memory footprint without sacrificing accuracy or safety.
• Collaborate with motion planning, controls, and perception teams to integrate ML components into the end-to-end autonomous driving stack.
• Build scalable deployment infrastructure including evaluation pipelines, model packaging, benchmarking, and automated validation.
• Validate model performance in both simulation and on-road testing, analyzing results and driving iterative improvements.
• Maintain production-quality code in C++ and Python
  
  

What We’re Looking For

• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• 5+ years of professional experience deploying ML systems in real-world robotics, embedded, or autonomous platforms.
• Experience with reinforcement learning.
• Strong software engineering skills in C++ and Python, with knowledge of modern development practices (code reviews, testing, CI/CD).
• Hands-on experience with ML frameworks (PyTorch, TensorFlow) and model optimization for deployment.
• Familiarity with GPU acceleration, or inference optimization (e.g., TensorRT, CUDA).
• Strong problem-solving skills and ability to debug complex systems under production constraints.
  
  

Bonus Points

• Experience with autonomous vehicle motion planning, control algorithms (MPC, LQR, PID), or reinforcement learning–based methods.
• Publications in relevant ML or robotics conferences (ICRA, NeurIPS, CoRL, RSS, etc.).
• Experience with ROS, AUTOSAR, or other real-time robotics frameworks.
• Knowledge of numerical optimization and its applications in trajectory generation.
  
  

Why You’ll Love Working Here

• Shape the real-world deployment of cutting-edge ML technology on autonomous vehicles.
  Work with world-class ML researchers, software engineers, and controls experts in a highly collaborative environment.
• Contribute to a mission-driven company building safe, accessible, and transformative mobility solutions.