Drimva

NVIDIA

Distinguished Engineer, Power Architecture

$320K–$489K

US, CA, Santa Clara · Principal · Full-time · Posted May 14, 2026

Experience
18+ years of relevant industry and/or academic experience.
Education
MSEE, MSCE, or PhD or equivalent experience in Electrical Engineering, Computer Engineering, Computer Science, or a related field.

About the job

Sets long-term power architecture strategy and leads performance-per-watt design, modeling, and optimization across NVIDIA’s silicon and systems portfolio.

What you'll do

  • Set cross-portfolio, multi-generation power strategy and guide performance-versus-power trade-offs and product roadmaps.
  • Lead silicon and vendor/IP co-architecture and end-to-end thermal and system-level power management.
  • Drive ML- and workload-aware modeling, post-silicon optimization, technical leadership, and industry influence.

What they ask for

  • MSEE, MSCE, or PhD, or equivalent experience demonstrating significant impact in a relevant field.
  • Demonstrated ownership of power architecture across several successful silicon generations.
  • Deep applied expertise in low-power and energy-efficient design, and authoritative knowledge of modern compute architectures, interconnects, memory, and system layers.
  • Proven development and implementation of power/performance modeling methods, metrics, and tools, plus experience architecting power solutions with internal teams and external partners.

Nice to have

  • Power and thermal architecture for a high-volume GPU, accelerator, SoC, or server SoC; or model-to-hardware co-optimization for ML, perception, or graphics workloads.
  • Co-design experience with platform power delivery and cooling; applied ML research or production work in power/performance; or deep implementation expertise and technical mentorship.

Pay

320,000 USD - 488,750 USD (base salary)

Tools

GPU, CPU, NVLink, PCIe, Ethernet, HBM, LPDDR, on-die SRAM

AI focus

Drives ML- and workload-aware modeling and post-silicon optimization; preferred experience includes ML workload co-optimization and applying ML to power/performance modeling, workload characterization, or DVFS/AVFS strategies. NVIDIA also uses AI tools in recruiting.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)