Senior Staff Engineer - AI s & Storage

Please Note: To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.

Please Note: To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.

Please Note: To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.

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At the Technology Enabling Development Lab (TED), our core development focus is the host interface firmware layer that sits in the intersection of system software and flash management firmware. This key host interface firmware technology drives Samsung’s breakthrough V‑NAND technology and enables our customers to power performance‑oriented, demanding, enterprise‑class applications ranging from hyper‑scale data centers, to big data processing, to software‑defined virtualized storage arrays and infrastructures.

We are building the next generation of NAND/SSD storage systems designed for the demands of large‑scale AI. As the compute cost of transformer inference falls, the bottleneck is shifting to how quickly and efficiently we can move model weights, KV cache, and activations through the storage hierarchy - and NAND flash and SSDs are increasingly the tier where that data lives.

Our focus is on making SSDs first‑class citizens in the AI data path, from the NAND media and flash‑translation layer up through NVMe and networked storage.

Sri Staff Engineer:
We are looking for a Sr Staff Engineer who lives at the intersection of AI inference systems and storage/systems software. This is a hands‑on technical leadership role: you will characterize real AI workloads, translate what you learn into architecture, and drive that direction across inference, platform, and hardware teams. This is a rare seat for someone who is equally comfortable reading a transformer serving stack and a Linux block‑layer trace.

• 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‑drive performance analysis spanning the inference runtime, the Linux storage and networking stack, and the underlying hardware, tuning for latency, throughput, cost, and GPU utilization.
• Build and evaluate transactional and system‑level models of proposed architectures to de‑risk decisions before hardware exists, and validate them against measured behavior.
• Engage with the standards and open ecosystem - SNIA (including Storage.AI), MLCommons/MLPerf, and the open inference stack - to align our work with where the industry is heading and to shape it where we can.
• Set technical direction others build on. Make build‑vs‑buy and…