Staff Engineer, SSD Storage and Systems Architecture
SSI San Jose full time
On Site
senior
$163k–$253k
H1B: yes
Responsibilities
AI-Optimized Architecture Design: Define and model enterprise/DC SSD architectures optimized for massive parallel read paths required by AI data loaders (e.g., PyTorch, TensorFlow) and high-burst write paths for AI model checkpointing.
Samsung Product Integration: Translate complex hardware, CXL, and controller constraints into optimized firmware architectures for Samsung’s enterprise SSD controller, FTL, and NAND flash subsystems.
Cloud Infrastructure Alignment: Architect features tailored for hyperscaler AI infrastructure, matching specifications like Microsoft’s Azure AI Cloud storage architectures and Meta’s next-generation GPU-accelerated storage platforms.
HW/FW Co-Design: Collaborate closely with ASIC design, firmware development, and validation teams to resolve cross-domain architectural bottlenecks in ultra-low latency environments.
Technology Prototyping: Develop architectural simulators or FPGA prototypes to evaluate next-generation storage features, AI caching algorithms, and host-managed storage paradigms.
Industry Influence & Innovation: Leverage an established technical network to align emerging system architectures with broader storage industry trends, open-source AI ecosystems, and storage standards (NVMe, OCP, SNIA).
Strategic IP Creation: Drive the creation of foundational architectural patents in the domains of AI storage optimization and near-data processing to protect Samsung's system-level intellectual property.
Hyperscale AI Engagement: Jointly architect next-generation AI datacenter deployment models (e.g., custom pooling, disaggregated storage, GPUDirect Storage [GDS] integrations, and Ethernet-attached SSDs) directly with lead architects from AWS, Google Cloud, Microsoft, and Meta.
Advanced Feature Pioneering: Lead the architectural definition of advanced storage paradigms such as FDP (Flexible Data Placement), ZNS (Zoned Namespaces), and computational storage specifically tailored to offload AI data-preprocessing tasks.
Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field with 10+ years experience, or a Master's degree with 8+ years experience, or a PhD with 5+ years experience.
Minimum of 10+ years of direct experience in storage system architecture, including foundational hands-on experience with legacy HDD system architecture and caching algorithms alongside modern NAND flash, SSD architecture, and AI/ML data pipeline storage.
Industry Influence: Verified track record of architectural leadership, technical contributions to open-source storage/AI projects, or technical influence within the broader storage ecosystem.
