Hybrid This role is categorized as hybrid. This means the successful candidate is expected to report to the GM Global Technical Center - Cole Engineering Center Podium or Mountain View Technical Center , CA at least three times per week, at minimum or other frequency dictated by the business. This job is eligible for relocation assistance.
About the Team:
The ML Compute Platform is part of the AI Compute Platform organization within Infrastructure Platforms. Our team owns the cloud-agnostic, reliable, and cost-efficient compute backend that powers GM AI. We’re proud to serve as the AI infrastructure platform for teams developing autonomous vehicles (L3/L4/L5), as well as other groups building AI-driven products for GM and its customers. We enable rapid innovation and feature development by optimizing for high-priority, ML-centric use cases. Our platform supports the training and deployment of state-of-the-art (SOTA) machine learning models with a focus on performance, availability, concurrency, and scalability. We’re committed to maximizing GPU utilization across platforms (B200, H100, A100, and more) while maintaining reliability and cost efficiency.
About the Role:
We are seeking a Staff ML Engineer to help build and scale robust compute platforms for ML workflows. In this role, you’ll work closely with ML engineers and researchers to ensure efficient model training and seamless deployment into production. This is a high-impact opportunity to influence the future of AI infrastructure at GM.
You will play a key role in shaping the user-facing experience of the platform, ensuring that ML practitioners can discover, schedule, and debug jobs with ease. The ideal candidate brings experience in designing distributed systems for ML, strong problem-solving skills, and a product mindset focused on platform usability and reliability.
What you’ll be doing:
- Design and implement core platform backend software components
- Experience cloud platforms like GCP, Azure or on-prem
- Collaborate with ML engineers and researchers to understand platform pain points and improve developer experience
- Thrive in a dynamic, multi-tasking environment with ever-evolving priorities. Interface with other teams to incorporate their innovations and vice versa
- Analyze and improve efficiency, scalability, and stability of various system resources
- Lead large-scale technical initiatives across GM’s ML ecosystem
- Help raise the engineering bar through technical leadership and best practices
- Contribute to and potentially lead open source projects; represent GM in relevant communities
