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# GPU Platform Infrastructure Engineer

- **Company:** Optimal Inc.
- **Location:** Warren, MI, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Systems Engineering, Microsoft Azure, Bash Shell, Computer Engineering, Continuous Integration, Linux, DevOps, Identity and Access Management, Python (Programming Language), Kubernetes, Infrastructure Automation Frameworks, Information Technology, Integration Frameworks, Tools for Reporting, Hardware Infrastructure, Docker
- **Published:** June 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f3697a652ad0ee07

## About the Role

Do you have experience in Tooling?, Do you have a Master's degree?, 3+ years of experience in Platform Engineering, Infrastructure Engineering, DevOps, or related field
Bachelor's or Master's degree in Systems Engineering, Computer Science, Computer Engineering, or related discipline, Experience with Linux, Kubernetes, Docker, and GPU infrastructure environments
Knowledge of workload scheduling, resource management, and multi-tenant platform operations
Experience supporting AI/ML, simulation, or GPU-intensive engineering workloads
Experience with monitoring, observability, and reporting tools
Strong scripting and automation skills using Python, Bash, or similar languages
Familiarity with NVIDIA GPU platforms, containerized compute environments, and infrastructure automation tools
Experience with CI/CD pipelines and cloud platforms such as AWS, Azure, or GCP is a plus
Experience with GUI development frameworks is a plus
Strong troubleshooting, documentation, and operational support skills

## Description

Support the GM ARC RTD team by building and maintaining the foundational GPU cluster platform infrastructure supporting shared AI/ML, simulation, and validation workloads. This role focuses on GPU access governance, resource allocation, scheduling policies, observability, and operational support for multi-tenant GPU environments including RTX 6000, A100, B200, and future systems., Manage GPU cluster access provisioning, onboarding, permissions, and lifecycle management
Design and maintain GPU resource allocation policies, quotas, namespace isolation, and scheduling configurations
Develop GPU utilization dashboards, reporting, monitoring, and capacity tracking solutions
Create reusable job submission templates and onboarding documentation for ML, Isaac Sim simulation, and validation workloads
Support platform governance, operational continuity, infrastructure scalability, and CI/CD integration
Design and develop GUI-based tools for streamlined Docker development workflows
Collaborate with infrastructure, AI/ML, and engineering teams to support shared GPU operations


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