Join NVIDIA, a pioneer in GPU technology and a leader in AI computing, and contribute to shaping the future of visual computing, automotive systems, and HPC.

As an Architect focusing on System Performance Verification and Analysis, you will immerse yourself in cutting-edge projects involving next-generation GPUs, high-performance CPU and memory subsystems, and advanced interconnect fabrics. You will play a crucial role in the entire pre- and post-silicon lifecycle, from performance modeling to silicon bringup.

This is an opportunity to amplify human creativity and intelligence by building the hardware that underpins the future of AI and computing. Be part of a diverse, supportive environment where innovation thrives and your contributions make a lasting impact.

Collaborate with cross-functional teams, including System Architecture and Design/Verification, to develop comprehensive performance test plans that mirror real-world product use cases.

Drive full-chip SoC performance verification using realistic concurrent workloads across various stages, including performance models, RTL simulation, emulation, and silicon. Identify and debug performance bottlenecks and regressions.

Influence architectural and microarchitectural decisions by providing early performance data and trade-off analysis to design teams. Develop and maintain performance workloads, test suites, and infrastructure, leveraging AI-assisted tools and automation to enhance efficiency and accuracy.

Improve verification methodologies to reduce turnaround times, enhance workload coverage, and enable earlier performance insights throughout the product development cycle.

A Bachelor's or Master's degree in Electrical Engineering, Computer Science, or a related field is required, alongside a minimum of 3 years of relevant experience in SoC or system-level architecture, performance verification, or hardware validation.

Possess a strong understanding of SoC architecture, including GPU/CPU pipelines, memory subsystems, Network-on-Chip (NoC) architecture, and high-speed I/O interfaces. Proficiency in Python and C/C++ is essential, along with scripting skills in Bash/Python for automation and analysis.

Demonstrate strong debugging, data analysis, and statistical analysis capabilities to derive actionable insights from complex performance data. Experience with pre-silicon performance analysis methodologies and excellent communication skills for effective collaboration in a global engineering organization are also key.