Senior AI Infrastructure Architect for Enterprise ISVs

NVIDIA Corporation

Santa Clara (CA)

On-site

USD 184,000 - 288,000

Full time

14 days+
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Job summary

NVIDIA Corporation in California is seeking outstanding AI Solutions Architects to help customers design, deploy, and optimize AI infrastructure using our latest accelerated computing technologies. You will partner with product, engineering, sales, developer relations, and partner teams to guide ISVs in adopting NVIDIA infrastructure for training, fine-tuning, inference, and deployment at scale.

Travel up to 20% may be required.

Qualifications

  • BS/MS/PhD in CS, EE, Physics, Math or related field, or equivalent experience.
  • 8+ years hands-on AI infrastructure, accelerated computing, distributed systems or ML platforms.
  • Experience designing and operating AI infrastructure at scale with orchestration and CI/CD.

Responsibilities

  • Partner with ISVs on discovery, architecture reviews, POCs, benchmarks, demos and deployment guidance.
  • Advise on design and optimization of accelerated AI infrastructure, including large-scale clusters.
  • Support compute, networking, storage, containers, observability, security and data center operations.
  • Drive monitoring, telemetry, and management tools to improve cluster utilization and performance.
  • Create reference architectures, deployment guides, sizing guidance and technical playbooks.
  • Travel up to 20% for customer meetings.

Skills

8+ years experience
AI infrastructure
distributed systems
CI/CD pipelines

Education

BS/MS/PhD in CS/EE/Physics/Math or related

Tools

OpenMPI
NCCL
Distributed training frameworks
GPU communication patterns

Job description

NVIDIA Corporation in California is seeking outstanding AI Solutions Architects to help customers design, deploy, and optimize AI infrastructure using our latest accelerated computing technologies. You will partner with product, engineering, sales, developer relations, and partner teams to guide ISVs in adopting NVIDIA infrastructure for training, fine-tuning, inference, and deployment at scale.

Travel up to 20% may be required.

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