Senior Systems Software Engineer – Accelerated Kubernetes Performance and Scale

NVIDIA

Senior Systems Software Engineer at NVIDIA focusing on Kubernetes performance and scalability for AI workloads. Leading technical solutions and optimization efforts across accelerated compute infrastructure.

Posted 6/30/2026full-timeSanta Clara • California, Washington • 🇺🇸 United StatesSenior💰 $152,000 - $241,500 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformKubernetesPython

About the role

Key responsibilities & impact

Lead end‑to‑end performance and scalability analysis across the Kubernetes‑based accelerated runtime stack (control and data planes)
Design and contribute upstream architectural changes to the Kubernetes control plane and related projects
Improve container startup and cold‑start latency to enable smooth, low‑latency inference scaling on Kubernetes across thousands of GPU nodes
Assess, improve, and contribute to open‑source projects that make Kubernetes an outstanding platform for AI workloads
Advance scalability and performance of confidential containers on Kubernetes
Use DSX and related large‑scale simulation infrastructure to model full AI‑factory deployments
Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at‑scale workload tests
Document methods and results clearly and present findings internally and at industry events

Requirements

What you’ll need

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

Benefits

Comp & perks

Equity
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Hard Skills & Tools

Computer ArchitectureNetworkingStorage SystemsAccelerator-Based PlatformsPerformance ModelingDistributed SystemsParallel ComputingAI Workload OptimizationCold-Start Latency ImprovementContainer Startup Optimization

Soft Skills

CollaborationDocumentationPresentation Skills