Senior Solutions Architect, GPU System
NVIDIA
full-time
Posted on:
Location Type: Office
Location: Beijing • China
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Job Level
Tech Stack
About the role
- Lead presales and architecture engagements with AI industry customers, focusing on GPU servers, AI clusters, and large-scale training/inference platforms built on NVIDIA HGX, GPU systems, and reference architectures
- Design and validate end-to-end AI data center solutions, including server platforms, storage connectivity, and high-performance networking based on Spectrum, Quantum, ConnectX, and BlueField
- Define system architectures for AI supercomputing, LLM training, and inference workloads, including node configuration, GPU topology, PCIe/NVLink considerations, and network design
- Support business teams in exploring, developing, and deploying NVIDIA server and GPU solution opportunities, from early technical discovery through POC and production rollout
- Own and execute POCs and hands-on labs that validate GPU server performance, scalability, reliability, and interoperability across compute, storage, and network domains
- Troubleshoot complex end-to-end issues involving GPU servers, firmware, drivers, operating systems, and networking stacks, and drive fixes with internal R&D and partners
- Provide structured feedback on platform features, system requirements, and customer needs to server OEMs, engineering, and product teams to improve NVIDIA AI platforms and ecosystems
Requirements
- BS/BA in Computer Science, Electrical/Computer Engineering, or equivalent experience
- 6+ years of experience with data center servers, GPU platforms, or large-scale AI/HPC infrastructure
- Strong understanding of GPU server architecture: CPU/GPU balance, memory and PCIe/NVLink topology, storage and NIC placement, and power/cooling considerations
- Proven experience designing or operating AI or HPC clusters using GPU-accelerated servers in cloud or on-prem data centers
- Solid background in data center and cloud networking for AI workloads, including leaf-spine fabrics, RDMA and high-bandwidth/low-latency designs
- Strong Linux system and Linux networking skills, including driver, firmware, and OS-level tuning for GPU and NIC performance
- Knowledge and experience with K8S, RDMA/RoCE and, ideally, RoCE and Infiniband AI clusters
- Excellent communication skills to collaborate with customers, server OEMs, and internal architecture and engineering teams
Benefits
- Competitive salaries
- Generous benefits package
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
GPU architectureAI data center solutionsAI supercomputingLLM traininginference workloadsdata center serversHPC infrastructureLinux system administrationKubernetesRDMA
Soft Skills
communicationcollaborationproblem-solvingfeedback provision
Certifications
BS in Computer ScienceBA in Computer ScienceBS in Electrical EngineeringBA in Electrical Engineering