Senior Performance Architect - Heterogeneous Workload Optimization

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.
• 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).

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