Distinguished Engineer, GPU Fleet Operations Automation

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5 · Remote

This role focuses on automating the operations of NVIDIA's DGX Cloud GPU fleet, including lifecycle management, health monitoring, and remediation. It involves defining and driving technical strategy for infrastructure operations across various environments and ensuring high availability and operational excellence.

What you'd actually do

  1. Various Architectural Work: define and drive the technical implementation for DGX Cloud operations practice for GPU fleet lifecycle.
  2. Collaborate on Cross Domain Disciplines: drive the technical strategy and awareness for best practices and technical capabilities into DGX Cloud engineering practices.
  3. Accelerate Integration: Guide the technical delivery into DGX Cloud across all delivery environments: enterprise, public cloud, and high security, isolated, sovereign.
  4. Engage Stakeholders: Collaborate with customers, infrastructure providers, and partners to ensure NVIDIA’s solutions set the industry standard for operational excellence.
  5. Full Software and System Lifecycle: From ideation to architecture, design, development, deployment, operations, and full lifecycle management, lead all technical aspects of planning and continuous evolution of large technical scope.

Skills

Required

  • 15-18+ overall years in technical roles with a focus on operations and automation for cloud infrastructure, platforms, and applications.
  • 5-10+ years of lead experience
  • BS/MS or higher or equivalent experience in systems / software engineering, or related engineering fields
  • Technical proficiency in multi-tenant data center and cloud-native architectures, with bare metal, virtualization, containerization, and higher level abstractions (IaaS, Kubernetes, Slurm), AI/ML platforms and applications.
  • Shown success delivering high-impact technically complex solutions that achieve high levels of transparency into resource utilization, performance, and operational insights.
  • Technical Leadership: Ability to synthesize multi-functional needs into architecture and design while guiding internal execution across complementary teams.
  • Communication and Partnership: Strong collaboration and influence skills, capable of leading engineering engagement, presenting with peers, partners, and working with high performance accelerated computing customers.

Nice to have

  • Application of Artificial Intelligence: Real world experience applying AI to component and system level issue identification and remediation.
  • Industry Expertise: Direct experience in designing, developing, delivering and operating highly available scaled out systems in enterprise and cloud environments.
  • Engineering Enablement: Demonstrated history of creating scalable processes and extensible systems that facilitate operations at scale.
  • Open Source Collaboration: Familiarity with open source ecosystems and projects. Ability to collaborate and influence in open source project governance, represent NVIDIA customers and partners interests in technical alignment and direction.

What the JD emphasized

  • high availability and operational standards
  • high-impact technically complex solutions that achieve high levels of transparency into resource utilization, performance, and operational insights
  • Application of Artificial Intelligence: Real world experience applying AI to component and system level issue identification and remediation.