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NVIDIA

Solutions Architect, Infrastructure

NVIDIA

Senior

$152,000 – $287,500

Listed on NVIDIA’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

NVIDIA seeks an Infrastructure Solutions Architect to guide deployment of next-generation data center GPUs and networking platforms at hyperscale, serving as a technical bridge between product teams and large enterprise customers. This role combines hands-on infrastructure expertise with cross-functional leadership to accelerate adoption of NVIDIA technologies globally.

Our summary, not NVIDIA’s wording. The full posting is on their site.

Skills this role names

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What they ask for

Required

  • BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or equivalent
  • 4+ years in Solutions Architecture or Infrastructure Engineering
  • Hands-on bring-up and validation of large-scale NVIDIA GPU platforms including multi-GPU and multi-node architectures
  • Understanding of high-performance networking technologies and their role in distributed AI workloads
  • Familiarity with NVIDIA system software stacks and performance tuning
  • Proficiency with Linux systems tools for debugging and performance evaluation
  • Understanding of server hardware architecture including PCIe, NUMA, firmware, and thermal/power management
  • Understanding of BMC/IPMI/Redfish for remote management and hardware debugging
  • Strong Linux fundamentals in drivers, kernel subsystems, and node-level performance analysis
  • Ability to identify performance bottlenecks across cluster, node, accelerator, network, and application layers

Nice to have

  • Outstanding interpersonal skills and ability to drive clarity across diverse technical teams
  • Knowledge of hyperscaler or cloud service provider infrastructure and networking primitives
  • Demonstrated leadership resolving multi-team infrastructure challenges
  • Record of taking GPU or infrastructure products from pilot to high-volume deployment in data centers
  • Familiarity with deep learning, LLM architectures, and distributed training/inference at scale

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