Engineer/Senior Machine Learning Integration Engineer

Motional is a driverless technology company focused on making autonomous vehicles a safe, reliable, and accessible reality. They are seeking a passionate engineer to deploy, optimize, and maintain machine learning-driven planning and control algorithms for real-time autonomous driving, collaborating across various teams to ensure model reliability in production.

Responsibilities:

• 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

Requirements:

• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field
• 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
• 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