Systems Software Engineer – Accelerated Kubernetes Performance and Scale

NVIDIA

Systems Software Engineer working on performance and scalability of Kubernetes-based accelerated runtime stack. Collaborating on architectural changes and contributing to open-source projects at NVIDIA.

Posted 7/17/2026full-timeSanta Clara • California, Washington • 🇺🇸 United StatesMid-LevelSenior💰 $108,000 - $178,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Kubernetes and performance optimization for AI workloads, with a strong foundation in computer architecture and distributed systems. Proficient in Golang and Python, capable of contributing to open-source projects and collaborating with diverse teams to enhance scalability and performance in cloud environments.

Highest-signal resume keywords

Kubernetes ExpertisePerformance OptimizationGolang ProficiencyAI Workload BenchmarkingCloud Provider Experience

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Applicant Tracking System Keywords

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Hard Skills

Performance ModelingDistributed SystemsComputer ArchitectureNetworkingStorage SystemsAccelerator-Based PlatformsAI Workload OptimizationCold-Start Latency ImprovementScalability AnalysisOpen-Source Contribution

Soft Skills

CollaborationDocumentationPresentation Skills

Tools & Technologies

NVIDIA Software StackCI/CD WorkflowsLarge-Scale Simulation InfrastructureKubernetes Control PlaneNVIDIA Components

Industry Keywords

CNCFHyperscale ClustersConfidential ContainersAI Factory DeploymentsKubeConGTC

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformKubernetesNode.jsPython

About the role

Key responsibilities & impact

Work on 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.

Requirements

What you’ll need

Recent graduate of a Bachelor’s, Master’s, or PhD degree in Engineering or equivalent experience, ideally in Electrical, Computer Engineering, or Computer Science
Experience in computer architecture, networking, storage systems, and accelerator‑based platforms
Expertise in Kubernetes and familiarity with the broader CNCF ecosystem
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
Familiarity with the NVIDIA software stack across training and inference
Experience with at least one major public cloud provider (for example, AWS, Azure, GCP, or OCI)

Benefits

Comp & perks

equity
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