Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale

Nvidia

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CASeattle, WA
Salary: $184,000–$287,500 / yr
Posted: 3 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $195k

This role $236k

$136k most similar roles pay here $304k

This role pays more than 85% of similar roles. Most pay $162,000–$228,950 — the shaded band above. At the midpoint, this role pays about $236k versus about $195k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 950 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 939 roles with salary data.

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At a glance

TL;DR · Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale

Role Posting Log in to save

As a Senior Systems Software Engineer at NVIDIA’s DGX Cloud organization, you will join a team dedicated to advancing AI infrastructure by addressing complex challenges in distributed systems. Your primary responsibilities include leading performance and scalability analysis across Kubernetes-based accelerated runtime stacks, designing architectural changes for reliable operation at hyperscale, improving container startup latency, and enhancing the efficiency of confidential containers on Kubernetes. You will also collaborate with researchers, developers, and customers to develop automated workload tests and integrate continuous performance testing into CI/CD workflows. The role requires expertise in Kubernetes, distributed systems, and GPU operators, along with proficiency in Golang or Python, and familiarity with major cloud platforms like AWS, Azure, or GCP. This position demands a deep understanding of the NVIDIA software stack and experience scaling Kubernetes clusters to ultra-large node counts, making significant contributions to open-source projects and industry standards.

Skills

Kubernetes Golang Python NVIDIA AWS Azure GCP OCI CNCF Docker CI/CD Prometheus PostgreSQL Git GitHub Linux CUDA TensorFlow PyTorch

What you'll do

Lead performance and scalability analysis across Kubernetes-based accelerated runtime stack, including NVIDIA components.
Design and contribute upstream architectural changes to Kubernetes control plane for reliable operation at hyperscale cluster sizes.
Improve container startup latency on Kubernetes to enable low-latency inference scaling across thousands of GPU nodes.
Assess and enhance open-source projects like Grove and gateway-api-inference-extension for AI workloads scalability and resilience.
Use DSX and large-scale simulation infrastructure to model full AI-factory deployments, validating scalability across simulated GPUs.

What we're looking for

8+ years of experience in computer architecture, networking, storage systems, and accelerator-based platforms.
Expertise in Kubernetes and familiarity with CNCF ecosystem tools and practices.
Deep experience with large-scale, parallel, distributed accelerator systems and AI workload performance optimization.
Proficiency in Golang or Python for system development and automation.
Strong operational experience with major public cloud providers like AWS, Azure, GCP, or OCI.
Demonstrated history of contributing to open-source projects and working collaboratively within communities.

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