Kubernetes Scale Systems Engineer for AI Infrastructure

NVIDIA; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

NVIDIA DGX Cloud is seeking a Systems Software Engineer to drive end-to-end performance and scale across the Kubernetes-based stack, including GPU Operator, Network Operator, DCGM, and distributed inference serving. You will work with AI researchers and customers to build automated tests, triage complex issues, and contribute to open-source communities.

You will design monitoring and analysis tools, participate in a modern CI/CD pipeline, and help shape scalable AI infrastructure while

Qualifications

• 2+ years in computer architecture, networking, storage or accelerators; degree in engineering or equivalent.
• Expertise in Kubernetes and CNCF projects; experience with large-scale accelerator-based systems.
• Proficiency in Golang or Python; experience with performance benchmarking at scale.

Responsibilities

• Drive end-to-end performance and scale characterization for the DGX Cloud stack across Kubernetes and NVIDIA components.
• Collaborate with researchers and developers to create automated tests simulating real workloads.
• Investigate performance and scale issues in distributed systems and identify root causes.
• Design monitoring, reporting and analysis tools for performance testing across resources.
• Triage and resolve issues in ultra-large Kubernetes clusters for reliability and efficiency.
• Build a CI/CD framework enabling continuous performance and scale testing.
• Document methodologies and present findings at internal/external venues like KubeCon and GTC.
• Engage with upstream communities (Kubernetes, CNCF, NVIDIA OSS) to validate workloads early.

Skills

Kubernetes

Performance modeling

Distributed systems

Golang/Python

Public CSPs

NVIDIA software

Education

Bachelors/Masters in Engineering

Tools

GPU Operator

DCGM

NIM

Job description

NVIDIA DGX Cloud is seeking a Systems Software Engineer to drive end-to-end performance and scale across the Kubernetes-based stack, including GPU Operator, Network Operator, DCGM, and distributed inference serving. You will work with AI researchers and customers to build automated tests, triage complex issues, and contribute to open-source communities.

You will design monitoring and analysis tools, participate in a modern CI/CD pipeline, and help shape scalable AI infrastructure while