Staff Engineer - AI Workload Benchmarking

Job Description

Onsite in San Jose, CA

Positioning:
• Align benchmarking insights to our 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.

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.