NVIDIA

4 weeks ago

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

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Lead end-to-end performance and scalability work for the Kubernetes-based accelerated runtime in DGX Cloud, spanning GPU operators, containers, and cloud integrations. You will push upstream architectural changes to Kubernetes for hyperscale clusters, optimize startup latency, contribute to open-source projects, and ensure secure, cost-efficient AI workloads across thousands of GPU nodes, partnering with researchers, customers, and upstream communities.

Location: Santa Clara

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key 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 and Network Operator, tracking issues from orchestration down to the metal.
•Design and contribute upstream architectural changes to the Kubernetes control plane to enable reliable operation at hyperscale cluster sizes in the open source ecosystem.
•Improve container startup and cold-start latency to enable low-latency inference scaling across thousands of GPU nodes while maintaining API server stability.
•Assess, improve, and contribute to open-source projects that enable Kubernetes for AI workloads, focusing on scalability, resilience, and multi-node training/inference.
•Advance scalability and performance of confidential containers (CoCo) on Kubernetes for encrypted inference workloads in production.

Pay and Benefits

Salary: USD 152,000 - 241,500

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•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

Experience:5+ yearsAI infrastructureKubernetesDistributed systemsCloud

Education:Bachelor's

Skills:CommunicationInterpersonal abilitiesProblem-solving

Tech Stack:KubernetesGolangPythonDockerNVIDIA software stackAWSAzureGCPOCICI/CDGPU OperatorNetwork OperatorGateway-api-inference-extension

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

4 weeks ago

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

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: Santa Clara

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key 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 and Network Operator, tracking issues from orchestration down to the metal.
•Design and contribute upstream architectural changes to the Kubernetes control plane to enable reliable operation at hyperscale cluster sizes in the open source ecosystem.
•Improve container startup and cold-start latency to enable low-latency inference scaling across thousands of GPU nodes while maintaining API server stability.
•Assess, improve, and contribute to open-source projects that enable Kubernetes for AI workloads, focusing on scalability, resilience, and multi-node training/inference.
•Advance scalability and performance of confidential containers (CoCo) on Kubernetes for encrypted inference workloads in production.

Pay and Benefits

Salary: USD 152,000 - 241,500

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•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

Experience:5+ yearsAI infrastructureKubernetesDistributed systemsCloud

Education:Bachelor's

Skills:CommunicationInterpersonal abilitiesProblem-solving

Tech Stack:KubernetesGolangPythonDockerNVIDIA software stackAWSAzureGCPOCICI/CDGPU OperatorNetwork OperatorGateway-api-inference-extension

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