We’re General Motors (GM), a company driving the future of mobility with advanced self-driving and electric vehicle technologies. We’re building the world’s most innovative autonomous vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many. GM employees have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there’s an opportunity to thrive in a variety of disciplines. This is a place for dreamers and doers to succeed. If you are looking to play a part in making a positive impact in the world by advancing the revolutionary work of self-driving vehicles, join us. About the team: The AV ML Infra team at GM builds end-to-end ML platforms and developers facing products designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques. Our ML infrastructure includes: AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. ML Compute: Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources. AV Pipelines & Lineage: Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility. Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization. Position Overview: As a Staff AI/ML Full-Stack Engineer, you will design and build end-to-end software products, owning everything from user-facing interfaces to backend services and cloud infrastructure. You will lead technically complex projects, collaborate closely with product managers and platform teams, and mentor junior engineers. This is a hands-on individual contributor role with a strong emphasis on technical depth, system design, and product impact rather than people management. Note: This role is part of an ML infrastructure engineering team and does not involve applying machine learning models for specific tasks. The focus is on developing infrastructure products that empower GM teams to perform machine learning and data science at scale.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees