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