Senior Performance Architect - Heterogeneous Workload Optimization

About the role

The Senior Systems Performance Engineer will architect and maintain custom profiling frameworks to measure, analyze, and optimize the interaction between extensive design graphs in system memory and high-throughput kernels on the GPU. This role involves deep-dive benchmarking of EDA applications to characterize memory access patterns, cache hit rates, and instruction-level parallelism. Additionally, the engineer will develop tools to monitor and attribute high-watermark memory usage in multi-terabyte EDA builds, focusing on opportunities for data structure compression or smarter memory pooling. The position also requires developing predictive models to guide hardware procurement and cloud instance selection based on built gate-count and algorithmic complexity.

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.

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).

Qualifications

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

Skills

• Proficiency in CUDA programming model.
• Experience with GPU profiling tools like NVIDIA Nsight Systems/Compute.
• Knowledge of profiling tools such as perf, eBPF, VTune, or Valgrind.
• Experience with distributed compute environments (Slurm, LSF, or Kubernetes).

Benefits

• NVIDIA offers highly competitive salaries and a comprehensive benefits package.
• Equity and benefits are also provided.

Pay

Base salary range: $184,000 - $287,500 for Level 4, and $224,000 - $356,500 for Level 5.

Schedule

Applications for this job will be accepted at least until February 16, 2026.