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

NVIDIA•Santa Clara, CA

4d•$184,000 - $356,500•Hybrid

About The Position

The DGX Cloud organization at NVIDIA brings together cutting-edge hardware and software innovation to deliver industry-leading accelerated computing for the world’s most ambitious AI workloads. We are a group of forward-thinking engineers tackling some of the globe’s toughest challenges, pushing progress, and positively affecting millions of lives. We’re searching for a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and systems performance and scalability. The ideal candidate brings broad, hands-on experience across the stack, including GPU operators, device plugins, distributed inference serving, and major cloud platforms. You’ll own hard technical problems at large scale and help shape how AI infrastructure runs in production. In this key role, you will focus on scaling AI infrastructure while minimizing total cost of ownership, reducing cost per token and enabling future AI innovation and AI factories.

Requirements

Bachelor’s or Master’s degree in Engineering or equivalent experience, ideally in Electrical, Computer Engineering, or Computer Science
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
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 (for example, AWS, Azure, GCP, or OCI)

Nice To Haves

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

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, doing in the open what today’s hyperscalers typically do privately.
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 (for example, 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 (for example, 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.