ML Engineer

We are seeking a highly skilled and experienced Staff Machine Learning Engineer to join our Mapping Engineering team. In this pivotal role, you will serve as a technical leader responsible for designing and implementing automated map reconstruction systems that operate at a national scale. Your expertise will enable the development of ML-driven pipelines that reconstruct, validate, and maintain critical map primitives such as lanes, boundaries, traffic controls, and signs from large-scale onboard sensor data. This position offers an exciting opportunity to shape the future of autonomous driving technology by leading cross-functional initiatives that integrate perception, localization, simulation, and infrastructure efforts. You will operate with high autonomy, define technical strategies in ambiguous problem spaces, and mentor senior engineers, elevating the organization’s ML and computer vision capabilities., * Architect and lead ML-driven map reconstruction systems that operate at a national scale using multi-modal sensor data (camera, lidar, radar, vehicle signals)

• Design and implement end-to-end pipelines for offline map reconstruction, including data mining, labeling strategies, model training, evaluation, and deployment
• Define technical strategy and system architecture for next-generation mapping capabilities, ensuring robustness, safety, and scalability
• Lead the adoption and development of state-of-the-art computer vision and ML techniques such as detection, segmentation, 3D reconstruction, and BEV representations applied to mapping problems
• Collaborate closely with Perception, Localization, Simulation, and Platform teams to define interfaces, data contracts, and integration points
• Drive technical excellence through design reviews, mentorship, and guidance for senior and staff-level engineers
• Diagnose and resolve system-level issues across data pipelines, ML models, and production workflows
• Serve as a Subject Matter Expert (SME) for ML-based mapping and reconstruction within the organization
• Contribute to technical roadmaps, hiring initiatives, and capability development for ML and CV expertise within the team

• 5+ years of experience in building and deploying machine learning or computer vision systems in production environments
• Strong foundation in computer vision, machine learning, or robotics, with hands-on experience designing and training ML models
• Proficiency in Python; familiarity with C++ or other systems languages is a plus
• Experience developing large-scale data pipelines for ML, including dataset curation, labeling workflows, training, and evaluation
• Proven ability to lead complex, cross-functional technical initiatives with high autonomy and influence
• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Robotics, or related technical fields, or equivalent industry experience
• Strong systems thinking skills and ability to reason about end-to-end ML systems

• Comprehensive health and wellbeing programs including medical, dental, and vision coverage
• Retirement savings plans and flexible spending accounts
• Paid vacation, holidays, and sick leave
• Tuition assistance and employee development programs
• Employee assistance programs and wellness initiatives
• GM vehicle discounts and participation in company vehicle evaluation programs
• Potential relocation benefits for eligible candidates

Equal Opportunity

General Motors (GM) is a global automotive leader dedicated to innovation, sustainability, and safety. With a rich history of engineering excellence, GM is at the forefront of developing advanced mobility solutions, including autonomous driving, electric vehicles, and next-generation mapping systems. Committed to creating a safer and more sustainable future, GM leverages cutting-edge technology and a diverse workforce to drive meaningful change in the transportation industry. The company’s vision encompasses Zero Crashes, Zero Emissions, and Zero Congestion, reflecting its dedication to making the world better, safer, and more equitable for all.