Senior Distributed Systems Engineer - AI Inference Factory

NVIDIA Corporation

California

On-site

USD 184,000 - 288,000

Full time

5 days ago
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Job summary

NVIDIA Corporation in California seeks a senior engineer to design and build factory infrastructure and automation for NVIDIA Inference Microservices (NIMs). You will create scalable, reliable automation pipelines to validate AI models across cloud, on-prem and edge deployments on NVIDIA GPUs.

You will mentor teams, define metrics, and collaborate with multiple model teams to accelerate deployment of performant, validated NIMs in heterogeneous environments.

Qualifications

  • Experience building distributed microservices and cloud software.
  • Ability to work with cross-functional teams and architects.
  • Mentorship and team growth experience.
  • Deep expertise in containerized apps using Docker, Kubernetes, Cloud Endpoints, Helm, Prometheus.
  • BS or MS in CS/ENG or related with 8+ years in relevant roles.

Responsibilities

  • Develop a factory pipeline to deploy AI models as validated services across Cloud, On-prem and Kubernetes environments.
  • Design interfaces, data models, and observability for the factory pipeline.
  • Collaborate with AI model teams to improve productivity and output of NIMs.
  • Define metrics and drive improvements; mentor teammates and grow capabilities.

Skills

distributed systems
backend services
microservices
cloud technologies
mentorship
leadership
debugging performance

Education

BS or MS in Computer Science/Engineering

Tools

Docker
Kubernetes
Cloud Endpoints
Helm
Prometheus

Job description

NVIDIA Corporation in California seeks a senior engineer to design and build factory infrastructure and automation for NVIDIA Inference Microservices (NIMs). You will create scalable, reliable automation pipelines to validate AI models across cloud, on-prem and edge deployments on NVIDIA GPUs.

You will mentor teams, define metrics, and collaborate with multiple model teams to accelerate deployment of performant, validated NIMs in heterogeneous environments.

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