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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Infrastructure Engineer (Compute) - **Company:** General Motors - **Location:** Mountain View, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $155,420.0 - $205,900.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Google App Engines, Microsoft Azure, Cloud Computing, Distributed Systems, Hardware-In-The-Loop Simulation, Machine Learning, AI Infrastructure, Graphics Processing Unit (GPU), High Performance Computing, Autoscaling, Caching, Backend, Hardware Acceleration, Machine Learning Operations, Programming Languages - **Published:** July 9, 2026 - **Apply:** https://www.juju.com/job/00000000geslb6 ## About the Role + 4+ years of industry experience, with a focus on high performance backend services. + Strongexpertisein Go, or other similar coding languages. + Experience working with cloud platforms such as GCP, Azure, or AWS. + Experienceindeliveringcross-functional initiatives. + Strong communicationskills and a proven ability to drive cross-functional initiatives. + Ability to thrive in a dynamic, multi-tasking environment with ever-evolving priorities. It's** **Preferred If You Have + Hands-on experience with Cloud VM services Google Compute Engine. + Experience with hardware-in-the-loop validation systems. + Experience with high performance computing (HPC). + Experience working with or designing interfaces and clients for developer workflows. + Familiarity with telemetry, and other feedback loops to inform product improvements. + Familiarity with hardware acceleration (GPUs) and optimizations. ## Description 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 **Senior ML Infrastructure** engineer to help build and scale robust Compute platforms for Simulation 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. They will rapidly test and promote ideas, have strong problem-solving skills, and demonstrate a bias for action. You will play a key role in shaping the architecture, roadmap, and user experience of a robust service supporting our AI Validation / Simulation needs. The ideal candidate brings experience in designing distributed systems, strong problem-solving skills, and a get-it-done attitude. This is a high-impact opportunity to influence the future of AI infrastructure at GM. What** **you'll** **be doing: + Design and implement core platform backend software components. + Collaborate with Simulation engineers, ML engineers and researchers to understand critical workflows, parse them to platform requirements, and deliver incremental value. + Lead technical decision-making on Compute architecture, cloud capacity provisioning, caching, 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., _Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Mountain View, you are expected to report to that location three times a week, at minimum._ ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Fifty Shades of Kubernetes Autoscaling](https://www.wearedevelopers.com/videos/813-fifty-shades-of-kubernetes-autoscaling) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) ## Related Articles - [Got AI ideas but no money? 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