General Motors (GM) is driving the future of mobility with advanced self-driving and electric vehicle technologies. They are building the world's most innovative autonomous vehicles to safely connect people to the places, things, and experiences they care about. The AV ML Infra team at GM builds ML infrastructure 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. They enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques. Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite their path to commercialization.
Requirements
- Strong software engineering background
- Experience shipping production-quality software for complex projects
- Experience with ML platforms, Kubernetes, or distributed computing
- Familiarity with autonomous vehicle ML workflows
Responsibilities
- Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.
- Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.
- Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.
- Lead and mentor engineers within the AV ML Infra team
- Hire and grow engineers, supporting career development and team expansion
- Ensure smooth execution by organizing team structure and processes effectively
- Promote engineering best practices and maintain high-quality product standards
Other
- 2-5 years of management experience (leading a engineering team)
- BS in Computer Science, Electrical Engineering, or related technical field
- Comfortable collaborating in a fast-paced, dynamic environment
- Strong analytical and problem-solving skills
- Collaborate with other GM teams to enable safe, reliable, and efficient ML workflows
