Senior Performance Architect - Heterogeneous Workload Optimization

NVIDIA•Durham, NC

12d•$184,000 - $356,500•Hybrid

About The Position

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!

Requirements

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.

Responsibilities

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.