Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale - DGX Cloud

NVIDIA has been at the forefront of computer graphics, PC gaming, and accelerated computing for over 25 years. Driven by technology and talented individuals, the company is now defining the next era of computing by harnessing the potential of AI with its GPUs. NVIDIA is committed to fostering an inclusive work environment, valuing diversity, and is an equal opportunity employer. They utilize AI tools in their recruiting processes and offer flexibility through hybrid and remote work arrangements.

Introduction

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for over 25 years. We are now leveraging the power of AI to define the next era of computing. The DGX Cloud organization at NVIDIA combines cutting-edge hardware and software innovation to deliver industry-leading accelerated computing for ambitious AI workloads. We are seeking a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and systems performance and scalability. This role involves tackling technical challenges at scale, shaping AI infrastructure, and focusing on optimizing AI infrastructure for minimal total cost of ownership and maximal AI innovation.

Responsibilities

• Lead end-to-end performance and scalability analysis across the Kubernetes-based accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node-feature-discovery, topograph, dra-driver-nvidia-gpu, and nvsentinel, tracking issues from orchestration down to the metal.
• Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes.
• Improve container startup and cold-start latency to enable smooth, low-latency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.
• Assess, improve, and contribute to open-source projects that make Kubernetes an outstanding platform for AI workloads (e.g., Grove and gateway-api-inference-extension), composing their architectures with scalability, resilience, and multi-node training/inference in mind.
• Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet stringent efficiency and latency requirements in production.
• Use DSX and related large-scale simulation infrastructure to model full AI-factory deployments and validate scalability across thousands of simulated GPUs, catching failures that emerge only at scale before hardware arrives.
• Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at-scale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.
• Document methods and results clearly and present findings internally and at industry events (e.g., KubeCon, GTC), while actively engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence and validate AI workload performance and scalability directions.

Requirements

• Bachelor’s or Master’s degree in Engineering (Electrical, Computer Engineering, or Computer Science) or equivalent experience.
• 5+ 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.
• Experience with performance modeling and benchmarking for large-scale systems.
• Proficiency in Golang and/or Python.
• Strong familiarity with the NVIDIA software stack across training and inference.
• Expertise with at least one major public cloud provider (e.g., AWS, Azure, GCP, or OCI).

Nice to Have

• Strong operational experience with any one of the Kubernetes distributions.
• Prior experience scaling Kubernetes clusters to ultra-large node and object counts.
• Demonstrated history of working in the open-source community.
• Excellent communication and interpersonal abilities.
• PhD or equivalent experience in relevant areas.

Benefits

• Eligibility for equity and comprehensive benefits (details on NVIDIA's benefits page).

About Company

NVIDIA has been at the forefront of computer graphics, PC gaming, and accelerated computing for over 25 years. Driven by technology and talented individuals, the company is now defining the next era of computing by harnessing the potential of AI with its GPUs. NVIDIA is committed to fostering an inclusive work environment, valuing diversity, and is an equal opportunity employer. They utilize AI tools in their recruiting processes and offer flexibility through hybrid and remote work arrangements.

How to Apply

Applications for this job will be accepted at least until July 3, 2026. Please apply through NVIDIA's official careers site.