ML Infrastructure Engineer; Compute

About the Team

The AI Validation Platform team owns the cloud‑agnostic, reliable, and cost‑efficient platform that powers GM’s AV efforts. We’re proud to serve as the infrastructure platform for teams developing autonomous vehicles (L3/L4/L5). Our platform supports the simulated validation of state‑of‑the‑art (SOTA) machine‑learning models, with a focus on performance, availability, concurrency, and scalability. We enable rapid innovation and development by prioritizing high‑impact, ML‑centric use cases.

We are seeking a Staff ML Infrastructure engineer to help build and scale robust compute platforms for simulation, data labeling, and data generation workflows. In this role, you will focus on scaling, driving efficiency, and high utilization of cutting‑edge GPUs, while also leveling up the platform’s reliability. The successful candidate will have experience building and running scalable distributed systems, rapidly testing and promoting ideas, strong problem‑solving skills, and a strong bias for action.

You will play a key role in shaping the architecture, roadmap, and user experience of a robust service supporting our ML simulation and hardware‑in‑loop validation needs. The ideal candidate brings experience in designing distributed systems, strong problem‑solving skills, and a product mindset focused on platform efficiency and reliability. This is a high‑impact opportunity to influence the future of AI infrastructure at GM.

• Collaborate with Simulation engineers, ML engineers and researchers to understand critical workflows, parse them to platform requirements, and deliver incremental value.
• Own the technical roadmap, lead technical decisions on compute architecture, caching, capacity provisioning, and auto‑scaling mechanisms.
• Drive the development of monitoring, observability, and metrics to ensure reliability, performance, and resource optimization.
• Proactively research and integrate frameworks, hardware accelerators, and distributed computing techniques.
• Lead large‑scale technical initiatives across GM’s ML infrastructure.
• Raise the engineering bar through technical leadership and by establishing best practices.

• 8+ years of industry experience, with a focus on high performance backend services.
• Strong expertise in container technologies like Docker and Kubernetes.
• Strong expertise in Go, or other similar coding languages.
• Experience working with cloud platforms such as GCP, Azure, or AWS.
• Experience in delivering cross‑functional initiatives.
• Strong communication skills and a proven ability to drive cross‑functional initiatives.
• Ability to thrive in a dynamic, multi‑tasking environment with ever‑evolving priorities.

• Hands‑on experience with Cloud VM services Google Compute Engine.
• Experience with hardware‑in‑the‑loop validation systems.
• Experience with high performance computing (HPC).
• Familiarity with telemetry, and other feedback loops to inform product improvements.
• Familiarity with hardware acceleration (GPUs) and optimizations.

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