Senior AI Infra Engineer for Large-Scale ML Platforms

NVIDIA

Seattle (WA)

On-site

USD 224,000 - 357,000

Full time

43 hours ago
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Job summary

NVIDIA is seeking a senior DGX Cloud AI Infrastructure software engineer in Seattle to design, build, and maintain platforms enabling large-scale AI training, inference, and deployment. You will own tooling, resilience, and API integration across the DGX Cloud stack, working with a team that emphasizes learning, blameless postmortems, and continuous improvement.

You will apply Golang, Python, and C/C++ expertise, leverage Kubernetes and observability tools, and drive reliability metrics while

Qualifications

  • 8+ years of software infrastructure experience for large AI systems.
  • Bachelor’s degree in CS or equivalent.
  • Strong debugging from application to hardware level.
  • Proven track record in scalable distributed systems.
  • Experience with AI training/inference and data infrastructure.

Responsibilities

  • Develop platform and tools for large-scale AI, LLM, and GenAI infrastructure.
  • Optimize tools to improve AI/ML workload efficiency and resiliency.
  • Root cause analysis from app to hardware level.
  • Enhance infrastructure underpinning NVIDIA's AI platforms.
  • Co-design APIs for integration with resiliency stacks.
  • Define reliability metrics to track system/service reliability.
  • Collaborate across teams with strong communication.

Skills

Golang
Python
C/C++
Kubernetes
Observability

Education

Bachelor's degree in CS or related field

Tools

NCCL
ELK
Prometheus
Loki

Job description

NVIDIA is seeking a senior DGX Cloud AI Infrastructure software engineer in Seattle to design, build, and maintain platforms enabling large-scale AI training, inference, and deployment. You will own tooling, resilience, and API integration across the DGX Cloud stack, working with a team that emphasizes learning, blameless postmortems, and continuous improvement.

You will apply Golang, Python, and C/C++ expertise, leverage Kubernetes and observability tools, and drive reliability metrics while

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