NVIDIA

6 days ago

Systems Software Engineer, Accelerated Kubernetes Performance and Scale - New College Grad 2026

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Work on end-to-end performance and scalability across NVIDIA’s Kubernetes-based accelerated runtime stack for AI workloads. Design and contribute upstream changes to the Kubernetes control plane, improve container startup and cold-start latency, and strengthen scalability and performance for confidential containers. Build and validate large-scale simulations with DSX, collaborate with researchers and communities, and integrate continuous performance/scale testing into modern CI/CD workflows.

Location: Santa Clara, Seattle

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Seniority: Entry level

Key Responsibilities

•Perform end-to-end performance and scalability analysis across the Kubernetes-based accelerated runtime stack, from orchestration to the metal.
•Design and contribute upstream architectural changes to the Kubernetes control plane to enable reliable operation at hyperscale cluster sizes.
•Improve container startup and cold-start latency for low-latency inference scaling across thousands of GPU nodes.
•Assess and contribute to open-source projects that make Kubernetes an outstanding platform for AI workloads, including scalability and resilience architectures.
•Use DSX simulation infrastructure to model AI-factory deployments, validate scalability across thousands of GPUs, and integrate continuous performance/scale testing into CI/CD workflows.

Pay and Benefits

Salary: USD 108,000 - 195,500 annually

Equity and Bonus:Equity

Key Requirements

•Recent graduate with 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 CNCF ecosystem.
•Experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads.
•Proficiency in Golang and/or Python and experience with performance modeling and benchmarking for large-scale systems.

Experience:AI workloadsDistributed systemsAccelerated computingKubernetesOpen source

Education:Bachelor's

Skills:CommunicationInterpersonal abilities

Tech Stack:KubernetesCNCFGolangPythonAWSAzureGCPOCIDSXGPU OperatorNetwork OperatorNode-feature-discoveryTopographDra-driver-nvidia-gpuNvsentinelGroveGateway-api-inference-extensionConfidential containers (CoCo)CI/CDMonitoring

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

6 days ago

Systems Software Engineer, Accelerated Kubernetes Performance and Scale - New College Grad 2026

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Location: Santa Clara, Seattle

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Seniority: Entry level

Key Responsibilities

•Perform end-to-end performance and scalability analysis across the Kubernetes-based accelerated runtime stack, from orchestration to the metal.
•Design and contribute upstream architectural changes to the Kubernetes control plane to enable reliable operation at hyperscale cluster sizes.
•Improve container startup and cold-start latency for low-latency inference scaling across thousands of GPU nodes.
•Assess and contribute to open-source projects that make Kubernetes an outstanding platform for AI workloads, including scalability and resilience architectures.
•Use DSX simulation infrastructure to model AI-factory deployments, validate scalability across thousands of GPUs, and integrate continuous performance/scale testing into CI/CD workflows.

Pay and Benefits

Salary: USD 108,000 - 195,500 annually

Equity and Bonus:Equity

Key Requirements

•Recent graduate with 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 CNCF ecosystem.
•Experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads.
•Proficiency in Golang and/or Python and experience with performance modeling and benchmarking for large-scale systems.

Experience:AI workloadsDistributed systemsAccelerated computingKubernetesOpen source

Education:Bachelor's

Skills:CommunicationInterpersonal abilities

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor