英伟达GPU Architect

• Investigate and design new hardware features for future graphics and parallel processing architectures. • Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models. • Develop tests, testplans, and testing infrastructure for new graphics or parallel processing architectures • Be hungry to learn and work on simulators, RTL and real silicon.

THE ROLE: “AI Product Applications Engineer (Solution Architect) – China” position is in the AMD AI group, located in China. THE PERSON: Success in this role will require deep knowledge of Data Center, Client, Endpoint AI workloads such as LLM, Generative AI, Recommendation, and/or transformer … AI cross cloud, client, edge… the candidate needs to have hands-on experiences with various AI models, end-to-end pipeline, industry framework (pytrouch, vLLM, SGLang, llm-d,Triton) / SDKs and solutions. KEY RESPONSIBILITIES: Position technical proposals / enablement to (blogs, tutorials, user guide…) AI SW developers and/or top customers. Provide significant contribution to AI SW developers / communities and/or customer PoC success. Drive AI developers / communities / customer requirements for AI SW, solution roadmap planning. Analyze competitive solutions to identify strength and weaknesses for articulating AMD AI SW & solution value propositions. Provide inputs / feedback to AI SW / hardware silicon / board roadmap for AI cross cloud, client, and edge...

• Investigate and design new hardware features for future graphics and parallel processing architectures. • Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models. • Develop tests, testplans, and testing infrastructure for new graphics or parallel processing architectures • Be hungry to learn and work on simulators, RTL and real silicon.

• 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.