Senior AI Infra Engineer - Kubernetes Scale & Performance

NVIDIA Corporation; Santa Clara (CA); Hybrid; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work

Job summary

NVIDIA DGX Cloud seeks a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and performance and scalability. You will own hard technical problems at large scale, optimizing AI infrastructure in production and shaping how AI workloads run on Kubernetes across thousands of GPU nodes.

You will collaborate with researchers, developers, customers, and upstream communities, contribute to open-source projects, and design automated workload tests, while

Qualifications

• Bachelor’s or Master’s degree in Engineering or equivalent experience.
• 8+ years of experience in computer architecture, networking, storage systems, and accelerator-based platforms.
• Expertise in Kubernetes and familiarity with the broader CNCF ecosystem.
• Deep experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads.

Responsibilities

• Lead end-to-end performance and scalability analysis across the Kubernetes-based accelerated runtime stack.
• Design and contribute upstream architectural changes to the Kubernetes control plane.
• Improve container startup and cold-start latency for thousands of GPU nodes.
• Assess and contribute to open-source projects for AI workloads.
• Advance scalability and performance of confidential containers on Kubernetes.

Skills

Kubernetes

Distributed systems

Golang/Python

Performance analysis

Education

Bachelor’s or Master’s degree in Engineering

Tools

Docker

Open-source ecosystems

Job description

NVIDIA DGX Cloud seeks a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and performance and scalability. You will own hard technical problems at large scale, optimizing AI infrastructure in production and shaping how AI workloads run on Kubernetes across thousands of GPU nodes.

You will collaborate with researchers, developers, customers, and upstream communities, contribute to open-source projects, and design automated workload tests, while