Platform Power Management Architect – Amd Instinct™ Gpus

AMD AMD · Semiconductors · Austin, TX · Engineering

This role focuses on defining and driving end-to-end power architecture for AMD Instinct data center GPU platforms. It involves system-level power strategy, including silicon, board-level power delivery, firmware, Linux power management, and rack-scale deployment. The architect will optimize performance per watt, ensure power integrity, and provide power projections for current and next-generation platforms, with a strong emphasis on AI/HPC workloads.

What you'd actually do

  1. Define the end-to-end power management architecture for AMD Instinct data center GPUs, spanning silicon, package, board, system, and rack levels.
  2. Lead power rail architecture and optimization, including rail partitioning, sequencing, voltage/frequency domains, and efficiency trade-offs.
  3. Define requirements and architecture for Linux-based power management, including interactions with kernel frameworks, drivers, firmware, and ROCm components.
  4. Develop and own power projection methodologies for GPUs, platforms, and multi-GPU systems across representative workloads.
  5. Incorporate scale-up (e.g., high-bandwidth GPU interconnects) and scale-out (e.g., networking fabrics) considerations into platform power strategy.

Skills

Required

  • platform, system, or silicon architecture with significant focus on power management
  • power delivery networks (PDN), voltage regulation, rail optimization, and power integrity fundamentals
  • Linux power management concepts, kernel/driver interactions, or system-level power control
  • building or consuming power models and projections for complex systems
  • work across hardware and software boundaries and influence architectural decisions

Nice to have

  • data center GPUs, accelerators, or high-performance SoCs
  • scale-up GPU fabrics
  • scale-out data center networking
  • telemetry, power capping, workload-aware power management, or fleet-level power optimization
  • presenting architectural trade-offs to senior technical leadership
  • HPC or AI training/inference systems

What the JD emphasized

  • power management
  • power delivery
  • power projection
  • AI/HPC workloads