AI Compute Hardware and Platform Lead

Skills

About the Role

Own the selection, qualification, and lifecycle management of GPU compute platforms, turning accelerator systems into dependable production infrastructure at scale.

Requirements

• Extensive experience building and operating large accelerator fleets
• Experience in hyperscale, cloud, HPC, or advanced systems environments
• Deep knowledge of server architecture, board-level integration, firmware risk, and fleet reliability engineering
• Track record with NVIDIA HGX or DGX, TPU infrastructure, custom accelerator programs, or dense rack-scale compute systems
• Strong understanding of manufacturing quality, service logistics, and infrastructure deployment at scale
• Ability to bridge lab-based engineering with global production operations
• Ability to mentor senior engineers on hardware systems thinking and fleet management

Responsibilities

• Own server qualification for AI compute platforms
• Lead procurement strategy and OEM relationships
• Lead BOM validation and platform bring-up
• Manage firmware lifecycles, repair strategies, sparing models, burn-in, acceptance testing, and end-of-life planning
• Establish fleet strategy across compute, interconnect, power, cooling, and management systems
• Build operating models for FRU inventories, RMA execution, depot workflows, and field replacement
• Create service strategies for board swaps, firmware rollback, and spare-part pooling
• Influence vendor roadmaps through technical engagement
• Build lifecycle decision frameworks for hardware expansion, sustainment, refresh, and retirement