英伟达GPU Senior Architect

• 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.

• Develop and maintain simulation environments built on frameworks like MuJoCo, and Isaac Lab to support robotics research. • Implement and test control algorithms and XR teleoperation interfaces for simulated robots. • Build procedural generation pipelines for diverse environments, object layouts, and robot motions. • Optimize GPU-based physics simulator performance for large-scale training workloads. • Import, configure, and validate robot assets in USD format, ensuring successful sim2real transfer. • Implement Sim2Real pipelines and deploy learned models to physical robots.

We are now looking for a Senior Solutions Architect, an outstanding engineer able to engage with developers, researchers, and decision makers. We need individuals who can use AI to improve system efficiency and develop close relationships with our industry customers, making NVIDIA a great part of end-user solutions.NVIDIA is the world leader in GPU accelerated computing and is looking for Solution Architects to engage our customers. The Senior Solution Architect will work closely with the industry customers in our China region - establishing relationships, solving problems with their engineering teams, and helping them to build a successful NVIDIA practice. If interested, do not hesitate to apply online, we are exciting to talk with you! What you’ll be doing: • Presenting NVIDIA’s full stack Artificial Intelligence solutions, and end to end platform technology to customers and partners. With in-depth hands-on engagements with customers or NVIDIA partners on complicated Datacenter projects. • Assist field business development in guiding the customer through the sales process for NVIDIA solution. • Understand and analyze customer requirements, support the solution design and development of applications. • Team work across the company to guide the direction of accelerated computing, working with software, research, and product teams. • Document the learnings to guide others. This can vary from making targeted training for customers and other Solutions Architects, giving nice hands-on demos, writing whitepapers, blogs, and wiki articles, recording short videos, to simply working through hard problems with a partner on a whiteboard.

• Design, implement, and optimize scalable ML training pipelines for training multimodal foundation models for robotics. • Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines. • Implement scalable data loaders and preprocessors for multimodal datasets, such as videos, text, and sensor data. • Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets. • Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters.