Staff ML Engineer, ML Compute Platform

Hybrid: This role is categorized as hybrid. 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, or as directed 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 serve as the AI infrastructure platform for teams developing autonomous vehicles (L3/L4/L5) and other groups building AI‑driven products. We enable rapid innovation and feature development by optimizing for high‑priority, ML‑centric use cases. The platform supports training and deployment of state‑of‑the‑art machine learning models with a focus on performance, availability, concurrency, and scalability. We commit 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 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 shape the user‑facing experience of the platform, ensuring that ML practitioners can discover, schedule, and debug jobs with ease. What you’ll be doing Design and implement core platform backend software components. Have experience with 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 viceversa. 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. Requirements 8+ years of industry experience. Expertise in either Go, C++, Python or other relevant coding languages. Strong background with Kubernetes at scale. Relevant experience building large‑scale distributed systems. Experience leading and driving large‑scale initiatives. Experience working with Google Cloud Platform, Microsoft Azure, or Amazon Web Services. Preferred Qualifications Hands‑on experience building ML infrastructure platforms with strong developer/user experience. Experience designing job orchestration interfaces, CLI tools, or web UIs for ML workflows. Familiarity with observability, telemetry, and user feedback loops to inform product improvements. Experience with GPU/TPU optimizations. Experience with training frameworks like PyTorch, TorchX. Experience with the Ray framework. Leadership/active participation in the open source community. Experience with infrastructure applications or similar experience. Compensation The expected base compensation for this role is: $195,000 – $298,000. Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. Benefits Medical, dental, vision Health Savings Account Flexible Spending Accounts Retirement savings plan Sickness and accident benefits Life insurance Paid vacation & holidays Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. All employment decisions are made on a non‑discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. #J-18808-Ljbffr