AI Workload Benchmarking Engineer
NUBYT, Inc.
- Location
- San Jose, CA, United States
- Track
- AI Infrastructure
- Level
- Staff
- Salary
- $135K–$156K / yr
- Posted
- October 3, 2026
- Source
- Glassdoor
Job description
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