GPU Platform Infrastructure Engineer

Optimal Staffing

Warren, MI

JOB DETAILS
SKILLS
Amazon Web Services (AWS), Artificial Intelligence (AI), Automation, Bash Scripting, Cloud Computing, Computer Engineering, Computer Science, Computer Systems, Continuous Deployment/Delivery, Continuous Integration, DevOps, Docker, Documentation, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), Graphical User Interface (GUI), Graphical User Interface (GUI) Design, Linux Operating System, Microsoft Windows Azure, Onboarding, Operational Support, Python Programming/Scripting Language, RTX, Reporting Dashboards, Resource Management, Scripting (Scripting Languages), Simulation, Systems Engineering
LOCATION
Warren, MI
POSTED
Today

Job Title: GPU Platform Infrastructure Engineer

Job Summary

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.

Required Experience
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

Responsibilities
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

Required Skills
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



About the Company

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Optimal Staffing