Principal Machine Learning Integration Engineer

Who you are

You have 5+ years of professional experience deploying machine learning systems in real-world robotics, embedded, or autonomous platforms — you've tackled challenges in optimizing models for performance under strict resource constraints. Your educational background includes a BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field, providing you with a solid foundation in both theory and practical application.

You possess deep expertise in C++ and Python, maintaining production-quality code while ensuring that ML-driven planning and control algorithms operate efficiently and safely. Your experience with reinforcement learning has equipped you with the skills to bridge advanced machine learning with safety-critical vehicle platforms, making you a key player in shaping the future of autonomy.

Collaboration is second nature to you — you thrive in environments where you can work closely with motion planning, controls, and perception teams to integrate ML components into the end-to-end autonomous driving stack. You understand the importance of communication and teamwork in achieving project goals and ensuring model reliability.

You are passionate about the challenge of deploying ML-based motion planning and control models onto vehicle platforms, ensuring performance under resource constraints without sacrificing accuracy or safety. Your analytical mindset allows you to validate model performance in both simulation and on-road testing, driving iterative improvements based on your findings.

Desirable

Experience with building scalable deployment infrastructure, including evaluation pipelines, model packaging, benchmarking, and automated validation would be a significant advantage. You are familiar with the latest trends in machine learning and autonomous driving, and you are eager to apply your knowledge to real-world challenges.

What you'll do

In this role, you will deploy ML-based motion planning and control models onto vehicle platforms, ensuring that they perform optimally under resource constraints. You will optimize models for inference speed, latency, and memory footprint, all while maintaining a strong focus on accuracy and safety. Your work will involve collaborating with various teams to integrate ML components into the autonomous driving stack, ensuring seamless operation across systems.

You will build scalable deployment infrastructure, which includes creating evaluation pipelines and model packaging processes. Your responsibilities will also involve benchmarking and automated validation to ensure that the models meet the required performance standards. You will validate model performance through rigorous testing in both simulation environments and real-world scenarios, analyzing results to drive continuous improvements.

Maintaining production-quality code in C++ and Python will be a key part of your daily tasks. You will be expected to contribute to the overall quality and reliability of the software, ensuring that it meets the high standards required for safety-critical applications in autonomous driving. Your role will also involve staying updated with the latest advancements in machine learning and applying them to enhance the capabilities of the systems you work on.

What we offer

Motional provides a collaborative and inclusive work environment where innovation is encouraged. You will have the opportunity to work on cutting-edge technology that is shaping the future of autonomous vehicles. We value diversity and are committed to creating an inclusive environment for all employees. You will be part of a team that is dedicated to pushing the boundaries of what is possible in the field of autonomous driving, making a real impact on the future of mobility.