CPU Storage Tech Lead
Leads technical strategy for CPU platforms, memory, and storage architectures in large-scale AI data centers. Evaluates vendor roadmaps, drives platform decisions, and ensures optimization for AI training and inference with 10+ years experience in server hardware and hyperscale infrastructure.
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.
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