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Senior Solutions Architect, CSP System

NVIDIA· Architect
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Shanghai, Chinafull-timePosted 1mo ago

About the role

Senior GPU & AI Infra Expert focusing on Cloud Service Providers (CSPs) in China, driving GPU/AI Infra strategy, system-level optimization, and high-value CSP engagements to accelerate deployment and performance of NVIDIA GPU software/hardware stacks for hyperscale data centers.

Responsibilities

  • Partner with Sales, BD and CPM teams to land NVIDIA GPU and AI Infra technologies into top-tier Chinese CSP accounts, drive technical penetration and sustainable business growth.
  • Serve as the primary technical authority for NVIDIA GPU system and AI infrastructure solutions for Chinese CSPs, providing end-to-end consultation on GPU cluster architecture design, AI workload deployment, heterogeneous computing tuning, and full-stack software stack optimization.
  • Unlock Vera CPU + GPU co-optimization value for RL training and Agentic AI workloads, eliminate CPU-GPU data movement bottlenecks, optimize end-to-end agent training and reasoning pipeline latency and throughput for CSP AI factory scenarios.
  • Lead open-source system architecture contributions for NVIDIA AI infra stacks, upstream optimized patches for key open-source projects, build China-localized best practices and shape industry technical standards.
  • Conduct in-depth GPU workload bottleneck analysis, implement system-level, kernel-level and framework-level tuning for AI training, inference, RL and gaming workloads, deliver production-ready reference designs and tuning guidelines for CSP mass deployment.
  • Act as the key technical liaison between Chinese CSP customers and NVIDIA global engineering, product and R&D teams, collect high-value local workload requirements, drive product roadmap iteration, and ensure full compliance with NVIDIA global technical policies and export compliance rules.
  • Lead technical workshops, hands-on training, PoC and production pilot projects for key CSP accounts, quantify and demonstrate GPU/AI Infra business value, accelerate technology adoption and large-scale replication.
  • Monitor cutting-edge industry trends including Agentic AI, LLM inference optimization, cloud gaming AI, and next-gen data center system architectures, output strategic technical insights to support team and product strategy formulation.
  • Mentor junior SA team members, standardize CSP technical engagement and solution delivery processes, and drive the precipitation of high-value technical best practices.

Qualifications

  • Bachelor’s/Master’s/PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field; equivalent industry experience is highly valued.
  • 7+ years of hands-on experience in GPU architecture, AI system optimization, large-scale data center infrastructure, or hyperscale cloud computing, with solid experience in AI training/inference, distributed computing or HPC workloads.
  • Deep understanding of GPU microarchitecture, CUDA programming model, GPU memory hierarchy and system scheduling mechanisms; proficient in performance profiling, bottleneck analysis and end-to-end AI workload tuning.
  • Strong programming proficiency in C/C++ and Python; familiar with CUDA kernels, compiler toolchains, 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 public cloud AI service architectures, cluster operation mechanisms and core workload characteristics.
  • Excellent technical communication and presentation skills, capable of explaining complex GPU system and AI infra technologies to technical engineers, architecture teams and business stakeholders.
  • Strong cross-functional collaboration capability, able to work efficiently in a global matrix team and prioritize multiple high-value technical projects under fast-paced business demands.
  • Familiar with NVIDIA full-stack products (GPU data center hardware, TensorRT-LLM, Dynamo, NCCL, CUDA software stack) is a significant plus.
  • Hands-on engineering capability is mandatory; candidate must be result-oriented, self-driven and able to independently own end-to-end technical project delivery.
  • Committed, proactive, and capable of sustaining high-quality technical output for long-term strategic CSP projects.

Skills

Required:GPU architecturePerformance profiling and bottleneck analysisC/C++ and Python

Benefits

  • With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers!
  • We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing.
Last updated Aug 28, 2026. Interested? Apply on the company site.

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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