Senior Staff Engineer - AI Workloads & Storage

What you'll do

• Profile AI inference, RAG, and training workloads and translate I/O needs into storage and memory-hierarchy decisions.
• Analyze data placement and storage-stack performance; develop and validate architecture models and customer proof-of-concepts.
• Set technical direction, establish benchmarking practices, mentor engineers, and collaborate across teams and industry ecosystems.

What they ask for

• Bachelor’s degree plus 15+ years, Master’s degree plus 13+ years, or PhD plus 10+ years of relevant industry experience.
• Extensive systems, storage, or ML-systems software experience, typically 10–15+ years, with a record of materially improving system performance, reliability, or cost.
• Working knowledge of transformer inference and deep understanding of the Linux storage stack and NAND/SSD internals, with hands-on performance-analysis skills.
• Fluency in Python and a systems language (C/C++, Rust, or Go); demonstrated technical leadership and cross-team influence.

Nice to have

• Hands-on experience with modern inference stacks, GPU-adjacent data movement frameworks, user-mode storage, or SSD firmware and data placement.
• Experience with AI workload characterization and benchmarking, system-level modeling, SSD interfaces, or large-scale GPU-cluster storage.

Pay

$189,000 — $301,000 USD base salary

Tools

NVMe Flexible Data Placement (FDP), Python, C/C++, Rust, Go, Linux, vLLM, SGLang

AI focus

Characterize and optimize AI inference, RAG, and training workloads; guide SSD and memory-hierarchy design and data placement for AI data paths.