Senior Performance Architect - Heterogeneous Workload Optimization

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

As EDA workloads transition from traditional CPU-bound tasks to massively parallel GPU-accelerated engines, the complexity of identifying bottlenecks has scaled exponentially. We are seeking a Senior Systems Performance Engineer to build our next generation of profiling infrastructure. You will be responsible for measuring, analyzing, and optimizing the interaction between extensive design graphs in system memory and high-throughput kernels on the GPU. Join us to push the boundaries of what's possible in the future of computing!

What you'll be doing:

• Architecting and maintaining custom profiling frameworks that provide a unified view of execution across CPU (multi-core/multi-socket) and GPU (multi-node/NVLink) environments.

• Conducting deep-dive benchmarking of EDA applications to characterize memory access patterns, cache hit rates, and instruction-level parallelism.

• Using GPU profilers to detect GPU-side inefficiencies such as warp divergence, sub-optimal occupancy, and PCIe/NVLink bottlenecks.

• Developing tools to monitor and attribute high-watermark memory usage in multi-terabyte EDA builds, finding opportunities for data structure compression or smarter memory pooling.

• Developing predictive models to guide hardware procurement and cloud instance selection based on built gate-count and algorithmic complexity.

What we need to see:

• A grasp of the CUDA programming model and experience employing GPU profiling tools like NVIDIA Nsight Systems/Compute to address PCIe bottlenecks and kernel stalls.

• Extensive knowledge of profiling tools such as perf, eBPF, VTune, or Valgrind, along with insight into their internal mechanisms.

• A passion for meticulous benchmarking and the ability to distill sophisticated performance data into actionable engineering roadmaps.

• Experience with distributed compute environments (Slurm, LSF, or Kubernetes).

• A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience) with more than 8+yrs of relevent experience and at least 5 years involved in systems-level performance analysis.

NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most brilliant and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you!

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.Applications for this job will be accepted at least until September 10, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.