Staff Engineer - AI Workload Benchmarking

Staff Engineer - AI Workload BenchmarkingAlign benchmarking insights to leadership in memory (HBM, DRAM, CXL) and storage (SSD/NAND) to inform product roadmaps. Enable next-generation AI infrastructure solutions through performance-driven system design, validation, and standards engagement.Requirements:Bachelor's/ Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field; advanced degree preferred.Professional experience in systems performance engineering, storage performance, or AI infrastructure benchmarking.Demonstrated expertise with NVMe SSDs and storage stack performance analysis (block layer, page cache, file systems, asynchronous I/O).Hands-on experience with AI/ML workloads — LLM training and inference frameworks (PyTorch, vLLM, TensorRT-LLM, or equivalent), embedding pipelines, or vector databases (FAISS, Milvus, DiskANN, HNSW).Strong proficiency with Linux performance and tracing tools: blktrace, perf, eBPF/bpftrace, ftrace, BCC, iostat, fio.Working knowledge of GPU systems and accelerator I/O paths.Experience designing and executing benchmarks against industry standards (MLPerf Storage, or equivalent).Proficiency in Python for benchmarking automation, data analysis, and visualization; comfort with C/C++ for systems-level work.Proven ability to deliver structured technical reports, characterization studies, and reproducible benchmark artifacts to a senior engineering audience.Location: San Jose, CAPR: $65.00 - $75.00/hr on W2Duration: 6+ Months