ML Platform Engineer - GPU Infrastructure

Job Title:

ML Platform Engineer - GPU Infrastructure

• Job Summary
• Support team by designing, implementing, and maintaining the automation and ML workload enablement layer of the GPU cluster platform. This role focuses on optimizing GPU compute environments for AI/ML training and Isaac Sim simulation workloads, integrating GPU jobs into CI/CD pipelines, standardizing runtime environments, and supporting reliable storage and artifact management.

Experience

• 3+ years of experience in ML Platform Engineering, DevOps, Infrastructure Engineering, or related field
• Bachelor's or Master's degree in Systems Engineering, Computer Science, Computer Engineering, or related discipline

Responsibilities

• Support GPU cluster platforms for AI/ML and simulation workloads
• Optimize GPU compute environments for ML training and Isaac Sim execution
• Integrate GPU workload execution into CI/CD pipelines
• Standardize runtime environments using containers and automation tools
• Manage storage, artifacts, and workload outputs
• Troubleshoot and improve platform reliability, scalability, and performance
• Collaborate with ML, infrastructure, and engineering teams

• Required Skills
• Experience with Linux, Kubernetes, Docker, and GPU infrastructure
• Knowledge of CI/CD tools and automation scripting (Python/Bash)
• Experience supporting AI/ML workloads and distributed systems
• Familiarity with NVIDIA GPU technologies and containerized environments
• Strong troubleshooting and performance optimization skills

• Preferred Skills
• Experience with Isaac Sim or simulation workloads
• Exposure to cloud platforms (AWS, Azure, or GCP)
• Knowledge of monitoring and observability tools such as Grafana or Prometheus