NVIDIA

3 weeks ago

Senior Solutions Architect, CSP System

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Serve as a core technical pillar in NVIDIA’s CSP Solutions Architect team, partnering with major Chinese cloud service providers to design and optimize GPU and AI infrastructure solutions. Own end-to-end consultation for GPU cluster architecture, AI workload deployment, heterogeneous tuning, and full-stack software optimization. Lead open-source system architecture contributions, perform deep performance/bottleneck analysis, and deliver PoCs and production pilots while aligning local requirements to NVIDIA’s global engineering roadmap.

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Partner with Sales, BD, and CPM to land NVIDIA GPU and AI infrastructure technologies into top-tier Chinese CSP accounts and drive technical penetration.
•Act as primary technical authority for GPU system and AI infrastructure solutions, providing end-to-end consultation on cluster architecture, AI workload deployment, heterogeneous computing tuning, and full-stack software optimization.
•Lead open-source system architecture contributions for NVIDIA AI infrastructure stacks, including upstream optimized patches and China-localized best practices.
•Conduct GPU workload bottleneck analysis and implement system-, kernel-, and framework-level tuning for AI training, inference, RL, and gaming workloads, delivering production-ready reference designs and guidelines.
•Run technical workshops, hands-on training, PoCs, and production pilots; mentor junior SA team members and serve as a liaison between CSP customers and NVIDIA global engineering, product, and R&D teams.

Key Requirements

•Bachelor’s/Master’s/PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (equivalent industry experience valued).
•8+ years of hands-on experience in GPU architecture, AI system optimization, large-scale data center infrastructure, or hyperscale cloud computing, including AI training/inference and distributed computing or HPC workloads.
•Deep understanding of GPU microarchitecture, the CUDA programming model, GPU memory hierarchy, and system scheduling; skilled in performance profiling, bottleneck analysis, and end-to-end AI workload tuning.
•Strong programming proficiency in C/C++ and Python; familiarity with CUDA kernels, compiler toolchains, and AI framework optimization (PyTorch/TensorRT) and large-scale distributed system tuning.
•Proven hands-on experience working with major Chinese CSPs or global hyperscalers, with in-depth knowledge of their cloud AI service architectures and cluster operation mechanisms.

Experience:8+ yearsHyperscale cloud computingDistributed computingHPCCloud service providers

Education:Bachelor's in Computer Science, Computer Engineering, Electrical Engineering, or related field

Skills:Technical communicationPresentation skillsCross-functional collaborationMentoringProblem-solving

Tech Stack:CC++PythonCUDACUDA kernelsCUDA software stackCompiler toolchainsPyTorchTensorRTTensorRT-LLMDynamoNCCLGPU microarchitectureCluster networkingOpen-source AI infra

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

3 weeks ago

Senior Solutions Architect, CSP System

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Partner with Sales, BD, and CPM to land NVIDIA GPU and AI infrastructure technologies into top-tier Chinese CSP accounts and drive technical penetration.
•Act as primary technical authority for GPU system and AI infrastructure solutions, providing end-to-end consultation on cluster architecture, AI workload deployment, heterogeneous computing tuning, and full-stack software optimization.
•Lead open-source system architecture contributions for NVIDIA AI infrastructure stacks, including upstream optimized patches and China-localized best practices.
•Conduct GPU workload bottleneck analysis and implement system-, kernel-, and framework-level tuning for AI training, inference, RL, and gaming workloads, delivering production-ready reference designs and guidelines.
•Run technical workshops, hands-on training, PoCs, and production pilots; mentor junior SA team members and serve as a liaison between CSP customers and NVIDIA global engineering, product, and R&D teams.

Key Requirements

•Bachelor’s/Master’s/PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (equivalent industry experience valued).
•8+ years of hands-on experience in GPU architecture, AI system optimization, large-scale data center infrastructure, or hyperscale cloud computing, including AI training/inference and distributed computing or HPC workloads.
•Deep understanding of GPU microarchitecture, the CUDA programming model, GPU memory hierarchy, and system scheduling; skilled in performance profiling, bottleneck analysis, and end-to-end AI workload tuning.
•Strong programming proficiency in C/C++ and Python; familiarity with CUDA kernels, compiler toolchains, and AI framework optimization (PyTorch/TensorRT) and large-scale distributed system tuning.
•Proven hands-on experience working with major Chinese CSPs or global hyperscalers, with in-depth knowledge of their cloud AI service architectures and cluster operation mechanisms.

Experience:8+ yearsHyperscale cloud computingDistributed computingHPCCloud service providers

Education:Bachelor's in Computer Science, Computer Engineering, Electrical Engineering, or related field

Skills:Technical communicationPresentation skillsCross-functional collaborationMentoringProblem-solving

Tech Stack:CC++PythonCUDACUDA kernelsCUDA software stackCompiler toolchainsPyTorchTensorRTTensorRT-LLMDynamoNCCLGPU microarchitectureCluster networkingOpen-source AI infra

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