R&D Lead & Inference Storage System Architect job opportunity at SanDisk.

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Date2026-04-28T07:24:47.775Z bot
SanDisk R&D Lead & Inference Storage System Architect
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Pattern: Full-time
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loacation Kfar Saba, Center District, Israel
loacation Kfar Saba, Cen..........Israel
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Job DescriptionWe are building a next-generation storage platform for AI infrastructure that combines high-performance flash, accelerator technologies, and advanced storage software, with the goal of delivering a breakthrough step-function improvement in cost, power efficiency, density, and scalability for AI-era data-center storage. We are seeking a R&D Lead & Inference Storage System Architect  to define and drive the end-to-end architecture of this platform, from data-center deployment models down to node-level HW/SW partitioning. This role will also contribute to strengthening Sandisk’s broader data-center infrastructure architecture knowledge across the Architecture organization, working collaboratively with existing domain experts and helping advance our system-level deployment thinking. Responsibilities:Drive the architecture of a groundbreaking data-center storage system targeting step-change improvements in cost, power, density, and scalabilityDefine the overall architecture of the storage platform and its deployment model within large-scale AI infrastructure environmentsAnalyze data-center deployment models, including hyperscaler environments, AI training and inference clusters, and disaggregated storage approachesDefine system data flows, metadata flows, and identify performance choke pointsArchitect the storage rack and tray topologyDefine the storage node architecture, including SoC/DPU selection, PCIe/NVMe topology, DRAM architecture, and NIC integrationDrive system partitioning decisions across hardware, firmware, and software componentsDefine placement of RAID, compression, and data servicesLead bottleneck analysis and scalability modeling at node and rack levels, including multi-node and large-scale system behaviorOwn system-level power and performance modelingEngage directly with customers and ecosystem partners to align architecture with real deployment needsGuide cross-company technical collaboration and ensure architectural alignment across internal and external contributors

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