AI Workload Benchmarking Engineer

Role: Staff Engineer - AI Workload Benchmarking

Location: San Jose, CA (Onsite)

Duration: 6 Months Contract

Client: Top Semiconductor Company

Job ID: 579601

Description:

Specific Positioning:

• Align benchmarking insights to Samsung's 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.

Pay: $65.00 - $75.00 per hour

Education:

• Bachelor's (Required)

Experience:

• systems performance engineering, storage performance: 2 years (Required)
• NVMe SSDs and storage stack performance analysis : 2 years (Required)
• AI/ML workloads — LLM training and inference frameworks : 2 years (Required)
• Linux performance and tracing tools: 2 years (Required)
• design and execute benchmarks against industry standards : 2 years (Required)
• Python for benchmarking automation and visualization: 2 years (Required)
• AI infrastructure benchmarking: 2 years (Required)

Location:

• San Jose, CA 95134 (Required)

Work Location: In person