CPU Storage Tech Lead

Key Responsibilities

• Own CPU and storage technical strategy for Stargate compute infrastructure across current and future generations.
• Evaluate CPU platforms across performance, efficiency, memory bandwidth, PCIe topology, cost, and roadmap alignment.
• Define storage architectures for AI environments, including boot media, local NVMe, shared storage, caching tiers, metadata services, and high-performance data pipelines.
• Drive server platform decisions involving CPU, memory, NIC, GPU, and storage subsystem integration.
• Partner with performance modeling teams to quantify tradeoffs across compute, memory, I/O, and storage bottlenecks.
• Work with silicon and hardware vendors on roadmap influence, feature requests, qualification plans, and technical escalations.
• Lead bring-up and validation efforts for new CPU and storage platforms in lab and production environments.
• Partner with networking and cluster architecture teams to optimize end-to-end node design and data movement.
• Support supply chain and sourcing teams with technical vendor assessments and second-source strategies.
• Drive reliability, serviceability, and fleet lifecycle planning for compute and storage platforms.
• Translate future AI workload requirements into infrastructure platform specifications.
• Provide technical leadership across cross-functional stakeholders and executive reviews.

Qualifications

• Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science, or related technical field; advanced degree preferred.
• 10+ years of experience in server hardware, systems architecture, data center infrastructure, or hyperscale compute platforms.
• Deep expertise in modern CPU architectures (x86, ARM, accelerator host systems) and server platform design.
• Strong understanding of memory systems, PCIe/CXL fabrics, NUMA behavior, and platform-level performance constraints.
• Experience with storage systems including NVMe, SSD qualification, RAID, distributed storage, object/file systems, or high-performance data pipelines.
• Experience evaluating hardware tradeoffs across performance, cost, power, thermals, and supply availability.
• Familiarity with GPU clusters and AI training/inference infrastructure strongly preferred.
• Experience working directly with OEMs, ODMs, silicon vendors (AMD, Intel), or storage vendors.
• Strong systems thinking with ability to connect component decisions to fleet-level outcomes.
• Excellent communication skills with the ability to influence engineering and executive stakeholders.
• Proven ability to operate in fast-moving, ambiguous environments with high ownership.

Preferred Skills

• Experience designing infrastructure for large-scale AI or HPC environments.
• Familiarity with CPU vendor roadmaps across AMD, Intel, and ARM ecosystems.
• Experience with distributed storage architectures supporting GPU clusters.
• Knowledge of fleet operations, hardware lifecycle management, and production deployments at scale.
• Prior experience in hyperscale cloud, AI infrastructure, or advanced compute environments.