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

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

CUDA programming modelGPU profilingbenchmarkingmemory access patternscache hit ratesinstruction-level parallelismdata structure compressionpredictive modelingperformance analysiscloud instance selection

Soft Skills

meticulous benchmarkingactionable engineering roadmaps