Qualifications
Key Responsibilities Deploy machine learning-based motion planning and control models onto vehicle platforms, ensuring optimal performance within resource constraints. Enhance models for inference speed, latency, and memory usage while maintaining accuracy and safety. Work closely with motion planning, controls, and perception teams to effectively integrate machine learning components into the autonomous driving framework. Develop scalable deployment infrastructure, including evaluation pipelines, model packaging, benchmarking, and automated validation. Conduct model performance validation through both simulation and real-world testing, analyzing outcomes to implement iterative improvements. Maintain high-quality production code primarily in C++ and Python. Preferred Qualifications BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related discipline. Over 5 years of professional experience in deploying machine learning systems within real-world robotics, embedded, or autonomous platforms. Proficient in reinforcement learning methodologies. Strong software engineering background in C++ and Python, with familiarity in modern development practices, including code reviews, testing, and CI/CD. Hands-on experience with machine learning frameworks such as PyTorch and TensorFlow, along with model optimization for deployment. Knowledge of GPU acceleration and inference optimization techniques (e.g., TensorRT, CUDA). Strong analytical skills and the ability to troubleshoot complex systems under production constraints. Desirable Skills Experience in autonomous vehicle motion planning and control algorithms (MPC, LQR, PID) or reinforcement learning methods. Publications in reputable ML or robotics conferences (ICRA, NeurIPS, CoRL, RSS, etc.). Familiarity with ROS, AUTOSAR, or other real-time robotics frameworks.
About the job
Join our innovative team at Motional as a Principal Machine Learning Integration Engineer, where your expertise will play a crucial role in advancing machine learning-based motion planning for autonomous vehicles. In this pivotal position, you will be responsible for deploying, optimizing, and maintaining machine learning algorithms that govern planning and control for real-time autonomous driving, ensuring they meet stringent performance and safety standards.
Your collaboration with cross-functional teams in motion planning, controls, and software engineering will be vital to ensure that our models operate reliably in production environments. If you are driven by the challenge of integrating advanced machine learning with safety-critical vehicle systems and aspire to influence the future of autonomy, we encourage you to apply.
About Motional
Motional is at the forefront of redefining mobility through the power of autonomous technology. We are committed to creating a safe and accessible autonomous future, working tirelessly to develop innovative solutions that integrate advanced machine learning with real-world vehicle platforms.
