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NVIDIA

Designs the chips and software that power AI

Distinguished Engineer - Power Architecture

Silicon & ASIC DesignConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A master's degree in Electrical Engineering, Computer Engineering, or Computer Science, or a PhD, or equivalent experience demonstrating significant impact in one of those fields or a related field.
  • At least 18 years of relevant industry and/or academic experience.
  • Demonstrated ownership of power architecture across several successful silicon generations in server-class, client/mobile-class, or thermally constrained system-on-chip platforms, with personal responsibility for decisions that significantly developed product outcomes.
  • Deep applied expertise in the full stack of low-power and energy-efficient design techniques.
  • Authoritative understanding of modern GPU, CPU, and accelerator architectures; high-speed interconnects including NVLink, PCIe, Ethernet, optical, and automotive networks; memory subsystems including HBM, LPDDR, and on-die SRAM; and system, operating system, runtime, firmware, and workload layers that determine real-world performance per watt.
  • A proven history of developing and implementing power and performance modeling methods, metrics, and tools widely used across an engineering organization, including projections before silicon creation and detailed validation after silicon production.
  • Demonstrated experience defining requirements with and architecting power solutions across internal silicon teams and external silicon, IP, foundry, or packaging partners.

Responsibilities

  • Set multi-generation, cross-portfolio power strategy.
  • Own performance per watt across server-, client-, and edge-class platforms.
  • Drive performance-versus-power analysis that influences major silicon trade-offs and product roadmap decisions.
  • Lead silicon and vendor/IP co-architecture.
  • Define new technology frontiers.
  • Architect thermal and system-level power management end to end.
  • Drive machine-learning- and workload-aware modeling and post-silicon optimization.
  • Influence across the company and the industry.
  • Develop the next generation of technical leaders.
  • Generate sustained intellectual property and intellectual contribution.

Desired Qualifications

  • Own the architecture for power and thermal development in a leading GPU, AI accelerator, mobile system-on-chip, automotive system-on-chip, robotics system-on-chip, or large-scale server system-on-chip with significant shipment volume.
  • Experience driving model-to-hardware co-optimization for perception, machine learning, or graphics workloads on power- and thermally constrained platforms.
  • Background co-designing silicon with platform-level power delivery and cooling, ranging from rack- and row-scale liquid or immersion cooling in datacenters to passively cooled and battery-constrained edge devices.
  • Published research or production work applying machine learning to power/performance modeling, workload characterization, or dynamic/adaptive voltage and frequency scaling strategies at scale.
  • Deep roots in register-transfer-level design, microarchitecture, or backend implementation, enabling fluency from architectural intent to build realization, with a history of mentoring engineers into Principal- and Distinguished-level roles and building durable technical communities in power, performance, or architecture.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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