Staff Machine Learning Engineer - Mapping

## About the Role As a Staff Machine Learning Engineer in the Mapping organization at General Motors, you will be at the forefront of developing next-generation mapping systems that utilize machine learning and computer vision to reconstruct and maintain map primitives from large-scale sensor data. This is a critical role that will directly impact the company's autonomous driving capabilities and its ability to operate reliably across national deployments and evolving road conditions. You will be working closely with cross-functional teams, including Perception, Localization, Simulation, and Infrastructure, to define technical strategies and lead the development of state-of-the-art ML and CV techniques.

The role entails a high level of autonomy, with the expectation that you will define technical strategies in ambiguous problem spaces and lead complex, cross-functional technical initiatives. You will also be responsible for mentoring senior engineers and helping to raise the bar for ML and CV expertise across the organization. Given the company's commitment to innovation and its focus on pushing the boundaries of what is possible in autonomous driving, this is an exciting opportunity for someone looking to make a real impact in the field.

The team you will be working with is dedicated to building national-scale, next-generation mapping systems that move beyond static HD maps toward automated, ML-driven map reconstruction pipelines. This is a hands-on technical leadership role that requires a strong foundation in computer vision, machine learning, and robotics, as well as the ability to lead cross-functional efforts and drive technical excellence.

## What You Will Do - Architect and lead ML-driven map reconstruction systems that operate at national scale using multi-modal sensor data.

• Design and implement end-to-end pipelines for offline map reconstruction, including data mining, labeling strategies, model training, evaluation, and production deployment.
• Define technical strategy and system architecture for next-generation mapping capabilities, balancing ML innovation with robustness, safety, and operational scalability.
• Lead the development and adoption of state-of-the-art computer vision and ML techniques applied to mapping problems.
• Own cross-functional technical initiatives, working closely with Perception, Localization, Simulation, and Platform teams to define interfaces, data contracts, and integration points.
• Drive technical excellence through design reviews, mentorship, and technical guidance for senior and staff-level engineers across teams.
• 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 Mapping and across the AV organization.
• Contribute to technical roadmaps, hiring, and capability building for ML and CV expertise within the Mapping org.

## What We Are Looking For - 5+ years of experience 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 for ML development; familiarity with C++ or other systems languages is a plus.
• Experience building 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.
• BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related technical field, or equivalent industry experience.
• Strong systems thinking - ability to reason about end-to-end ML systems, not just individual models.

## Nice to Have - Experience with mapping, localization, perception, or robotics systems, particularly in autonomous driving or mobile robotics.

• Hands-on experience with 3D perception, BEV representations, or multi-view geometry.
• Familiarity with AV sensor data (camera, lidar, radar) and real-world data challenges (noise, drift, long-tail scenarios).
• Experience deploying ML models into production pipelines with monitoring, validation, and iteration loops.

## Benefits and Perks - Flexible and remote work arrangements.

• Opportunity to work on cutting-edge technologies and contribute to the development of autonomous driving systems.
• Collaborative and dynamic work environment with a team of talented engineers.
• Professional development opportunities, including training and education programs.
• Competitive compensation and benefits package.
• Access to the latest tools and technologies in the field of machine learning and computer vision.
• Recognition and reward for outstanding performance and contributions to the company's mission.

- To stand out as a candidate, make sure your resume and online profiles (like LinkedIn) showcase your experience with machine learning, computer vision, and robotics, especially in the context of autonomous driving or mapping systems.

• Familiarize yourself with the company's current projects and initiatives in autonomous driving and next-generation mapping, and be prepared to discuss how your skills and experience align with these efforts.
• Highlight any experience you have with large-scale data pipelines, ML model deployment, and production workflows, as these are critical components of the role.
• Be ready to provide specific examples of times when you've had to lead cross-functional technical initiatives or mentor senior engineers, and discuss how you approach these challenges.
• Consider including a portfolio or samples of your work, such as research papers, open-source contributions, or personal projects, to demonstrate your expertise and capabilities in machine learning and computer vision.
• When negotiating salary, be aware of the current market rates for machine learning engineers in the automotive industry, and be prepared to discuss your expectations based on your experience and qualifications.