Teams at NVIDIA

Lead hardware-software co-design for LLM inference: drive and document architecture, build executable C++ hardware models, optimize performance with good algorithms and parallelism, diagnose cross-team chip and subsystem issues, and automate workflows to improve efficiency.

Leads onsite technical deployments of NVIDIA AI datacenter systems, integrating GPU, networking, and storage infrastructure. Troubleshoots complex hardware and deployment issues, supports cloud partners and enterprise customers, advises sales teams, and defines system architectures. Requires customer-facing technical leadership, datacenter expertise, networking knowledge, and deployment experience, with travel up to 50%.

Define high-level SoC subsystem and I/O architecture for LPU products, translate product requirements into architecture and IP specifications, build and validate functional/architectural models (C++, SystemC, Python), and drive tradeoffs across performance, power, reliability, and security while coordinating with IP, firmware, software, RTL, verification, and post-silicon teams.