Staff ML Engineer, ML Compute Platform

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.

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.

• →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

​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 with distributed systems
• →Experience leading and driving large scale initiatives
• →Experience working with Google Cloud Platform, Microsoft Azure, or Amazon Web Services

• Hands-on experience building ML infrastructure platforms with strong developer/user experience
• Experience working with or 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 Ray framework
• Leadership/active participation in the open source community
• Experience infrastructure applications or similar experience

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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

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