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NVIDIA

Senior Solutions Architect, CSP System

NVIDIA is hiring a Senior Solutions Architect, CSP System in Shenzhen, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Works on AI ●●●○ is how Level rates this solutions architect role: the daily work is on AI products, without building the model.
  • The posting mentions Grace Cpus, Nvl72 and cpu architecture among its tools and skills.
  • Level classes it as a senior role.
  • NVIDIA has 2,700+ other open roles on Level.

AI in this role

Drive CPU technical strategy and performance optimization for AI data centers and cloud service providers.

grace-cpusnvl72
cpu-architectureperformance-optimizationdata-centerhpcdistributed-computing

As a Senior CPU Expert focusing on Cloud Service Providers (CSPs) in China, you will be a core technical pillar in NVIDIA’s CSP team, responsible for driving CPU-related technical strategy, solution optimization, and customer engagement. You will work closely with major Chinese CSPs to address their CPU-centric needs in AI data centers, accelerate the deployment of NVIDIA’s integrated CPU-GPU-DPU platforms, and ensure optimal performance of customer workloads. This role requires deep expertise in CPU architecture, performance optimization, and data center infrastructure, as well as the ability to bridge technical requirements between CSP customers and NVIDIA’s global engineering teams.

What you'll be doing:

  • Work with Sales, BD and CPM team to introduce NVIDIA technologies into assigned accounts and grow business accordingly.

  • Serve as the primary technical authority on CPU technologies for NVIDIA’s Chinese CSP customers, providing expert consultation on CPU selection, architecture design, and integration with NVIDIA’s AI infrastructure (including Grace/Vera CPUs and NVL72 platforms).

  • Lead CPU-focused technical engagements with CSPs, collaborating with their R&D, infrastructure, and AI teams to understand workload requirements (e.g., AI data preprocessing, HPC, distributed computing) and design optimized CPU-GPU integrated solutions.

  • Drive CPU performance optimization for CSP workloads, conducting in-depth analysis of bottlenecks, implementing tuning strategies (including SIMD instruction set optimization and low-level intrinsics), and delivering reference implementations to unlock full platform potential.

  • Act as a liaison between CSP customers and NVIDIA’s global engineering, product, and R&D teams, advocating for customer-specific CPU requirements, providing feedback on product roadmaps, and ensuring alignment with NVIDIA’s technical strategy and export compliance guidelines.

  • Lead technical workshops, training sessions, and proof-of-concept (PoC) projects for CSPs, demonstrating the value of NVIDIA’s CPU-integrated solutions and enabling customer teams to effectively leverage these technologies.

  • Monitor industry trends in CPU technology, data center architectures, and CSP workload evolution, providing strategic insights to internal teams to enhance NVIDIA’s CPU-related products and solutions for the Chinese market.

  • Mentor junior technical team members, share CPU expertise, and drive best practices in CSP technical engagement and solution delivery.

What we need to see:

  • Bachelor’s/Master’s/PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field; equivalent industry experience will be considered.

  • 7+ years of hands-on experience in CPU architecture, performance optimization, or data center infrastructure, with a focus on high-performance computing (HPC) or AI workloads.

  • Deep expertise in CPU microarchitecture (e.g., x86, ARM), performance analysis tools, and optimization methodologies; proven track record of CPU benchmarking and bottleneck-driven tuning.

  • Strong programming proficiency in C/C++ and/or Python, with experience in low-level software optimization, compiler toolchains, or performance libraries.

  • Proven experience working with major Chinese CSPs or global hyperscalers, with a deep understanding of their technical requirements, infrastructure, and workload characteristics.

  • Excellent technical communication and presentation skills, with the ability to convey complex CPU and system-level concepts to both technical and non-technical stakeholders (customers, executives, internal teams).

  • Strong cross-functional collaboration skills, with the ability to work effectively in a matrixed global team and manage multiple priorities in a fast-paced environment.

  • Familiarity with NVIDIA’s data center products (GPUs, DPUs, CPUs) and software stacks is a significant plus; understanding of AI factory concepts and large-scale data center deployment is preferred. Hands-on ability is mandatory. Committed, independent, result-oriented.

Ways to stand out from the crowd:

  • Experience developing Linux kernel and virtualization technologies (e.g., KVM/QEMU, hypervisors, device passthrough/SR-IOV, VFIO), particularly enabling high-performance CPU-GPU-DPU platforms in virtualized and containerized CSP environments.

  • Experience with NVIDIA Grace/Vera CPUs or other ARM-based high-performance CPUs, and hands-on experience with integrated CPU-GPU-DPU platforms.

  • Experience with CPU in Agentic AI and Post-Training.

  • Background in AI/ML workload optimization, particularly in data preprocessing, distributed training, or inference pipelines on CPU platforms. Knowledge of export control reg ulations related to semiconductor and data center technologies, with experience navigating compliance requirements in the Chinese market.

  • Contributions to open-source performance tools, HPC frameworks, or CPU optimization libraries. Experience leading technical programs or cross-functional initiatives for CSP customers, including PoC delivery and large-scale deployment support.

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. If you're a creative and autonomous person with a real passion for technology, we want to hear from you.

How we rate this

Senior Solutions Architect, CSP System at NVIDIA rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Cpu ArchitecturePerformance OptimizationData CenterHpcDistributed ComputingGrace CpusNvl72

Also common in solutions architect jobs at this level

Pay

Typical solutions architect job
$169k

This posting doesn't list pay. Use the typical solutions architect figure when pay comes up. See solutions architect jobs

Questions you could be asked

  1. Tell me about a project where cpu architecture was part of your work. What did you do?
  2. Tell me about a project where performance optimization was part of your work. What did you do?
  3. Tell me about a project where data center was part of your work. What did you do?
  4. Tell me about a project where hpc was part of your work. What did you do?
  5. Tell me about a project where distributed computing was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Cpu Architecture, Performance Optimization, Data Center, Hpc, and Distributed Computing. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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