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As an HR Business Partner at NVIDIA, you will provide strategic HR support for the GPU Software business, focusing on staffing, performance management, and employee development.

Drive physical design of high-frequency, low-power CPUs/GPUs/SoCs at block, cluster, or full-chip level with emphasis on netlist quality: equivalence checking/formal verification, CDC and MTBF analysis, logic synthesis, timing convergence, STA, constraint generation/management, and ECO implementation. Use industry-standard EDA tools and scripting to debug and improve flows.

Design next-gen GPU systems, architect features with software and hardware teams, create functional models in C++, and support validation activities.

Lead implementation of AI infrastructure solutions on NCP/Neo Cloud and DGX Cloud. Deploy and optimize distributed training/inference, manage Kubernetes/GPU scheduling, profile and tune large-scale workloads, implement reference architectures and integrations, build runbooks and monitoring, and provide remote and on-site technical support to strategic customers.

Lead verification for GPU/SoC display IP: define verification scope and testplans, build UVM-based infrastructure, develop UVM and C/C++ testbenches, drive coverage closure, debug silicon integrations, and collaborate with architects, partners, and customers globally.

Lead NVIDIA's engagement with public-sector wireless research to advance 6G and AI-native RAN projects. Build partner relationships, represent NVIDIA in standards bodies (ATIS, AI-RAN, 3GPP), collaborate cross-functionally, analyze RAN challenges, and drive technical strategies and deliverables aligned with NVIDIA's platform and quality standards.

Lead and coordinate global software programs for deep learning training and inference frameworks. Drive cross-functional alignment, manage schedules, risks, and dependencies, implement release and open-source processes, define metrics, report status to senior management, and improve program execution across hardware, software, and infrastructure teams.

Apply LLMs, agentic systems, and reinforcement learning to front-end ASIC problems (RTL generation, verification, PPA). Build data generation, evaluation, and production ML tooling; integrate coding agents into EDA/validation flows; collaborate with model teams to fine-tune domain-specific models and deliver measurable speedups to ASIC schedules.

Perform silicon and package qualification for gaming and datacenter GPUs, define and validate test programs with Test/DFx, drive failure analysis across teams, analyze data to identify improvements, and support qualification timelines and risk management.

Lead NVIDIA's PCB and PCBA capability: set multi-year strategy and roadmap, build a world-class PCB/SMT team, prioritize investments, align suppliers, drive supplier execution and root-cause closure, raise manufacturing reliability using SPC/CpK/CTQ and inline metrology, and partner with design, SI, and reliability to qualify technologies for volume production.