Senior Staff Engineer - AI Workloads & Storage
Samsung Semiconductor, San Jose, California +1
What you will do
- Own AI workload characterization. Profile production and emerging LLM inference, RAG, and training workloads to quantify their I/O, bandwidth, latency, and capacity demands, and turn those findings into concrete storage and memory-hierarchy design decisions.
- Identify optimal data placement. Analyze workload access patterns to determine how data should be placed and separated on flash, and map those insights onto SSD data-placement technologies such as NVMe Flexible Data Placement (FDP) and streams to reduce write amplification and improve endurance, latency, and QoS.
- Collaborate with key customers to identify differentiating SSD capabilities for AI workloads, and develop proof-of-concept implementations as part of those customer engagements - turning workload insights into demonstrable data-path, tiering, and data-placement wins.
- Lead deep-dive performance analysis spanning the inference runtime, the Linux storage and networking stack, and the underlying hardware, tuning for latency, throughput, cost, and GPU utilization.