GPU Power Architect

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$100,000–$166,750 / yr
Posted
133 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k
This role $133k
$82k most similar roles pay here $264k

This role pays less than 92% of similar roles. Most pay $166,912–$241,750 — the shaded band above. At the midpoint, this role pays about $133k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · GPU Power Architect

As a GPU Power Architect - New College Grad 2026, you will join the Applied Power Architecture team to develop state of the art GPUs for AI, HPC, Automotive, GeForce, and Mobile products. You will contribute to power estimation models and tools for GPU products and systems like NVIDIA DGX while performing early architecture exploration focused on energy efficiency and TCO improvements at the datacenter level. Your daily work involves performance versus power analysis for future product lineups, deploying machine learning techniques to model GPUs, CPUs, and switches, and analyzing high-speed, high-density interconnects. You will utilize Python along with Pandas, NumPy, and PyTorch to analyze workload characteristics for GenAI and HPC workloads. Required expertise includes energy efficient chip design fundamentals, low power techniques like multi-VT, clock gating, and DVFS, and familiarity with performance monitors.

What you'll do

  • Develop power estimation models and tools for GPU products and systems like NVIDIA DGX.
  • Conduct early architecture exploration focused on energy efficiency and TCO improvements at the datacenter level.
  • Perform performance versus power analysis for future product lineups.
  • Deploy machine learning techniques to create accurate power and performance models for GPUs, CPUs, and switches.
  • Analyze workload characteristics for GenAI and HPC workloads to drive new hardware and software features.
  • Model and analyze cutting-edge technologies like high-speed and high-density interconnects.

What we're looking for

  • Pursuing or recently completed a Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, or equivalent experience.
  • Knowledge of energy efficient chip design fundamentals and related tradeoffs.
  • Familiarity with low power design techniques such as multi-VT, Clock gating, Power gating, and Dynamic Voltage-Frequency Scaling (DVFS).
  • Understanding of processors, system-SW architectures, and their performance/power modeling techniques.
  • Proficiency with Python and data analysis packages including Pandas, NumPy, and PyTorch.
  • Familiarity with performance monitors and simulators used in modern processor architectures.

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