Prodapt ASIC services (Formerly Innovative Logic)
Staff Engineer - AI Workload Benchmarking
Skills
2 months ago
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Important information
Contract type:
Freelance / Contract
Location:
San Jose, CA, USA
Work mode:
Onsite
Published on:
28 August 2026
What they need
Prodapt ASIC Services (formerly Innovative Logic) provides end-to-end SoC/ASIC, FPGA, and embedded software solutions. Our design services cover the complete lifecycle from RTL to GDSII, covering domains like 3nm node physical design, UVM verification, DFT, and silicon bring-up for 5G, AI, and IoT.
We are looking for Staff Engineer - AI Workload Benchmarking in San Jose, CA and it's a 5-day onsite position.
Job Description:
- Align 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.
Job Responsibilities:
- 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.
Thanks & Regards
Divakar Dhandapani
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