Power Methodology and Modeling Engineer

Nvidia

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
Austin, TX
Salary
$116,000–$189,750 / yr
Posted
129 days ago
Freshness
Confirmed live yesterday

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $182k
This role $153k
$104k most similar roles pay here $228k

This role pays less than 65% of similar roles. Most pay $148,624–$216,250 — the shaded band above. At the midpoint, this role pays about $153k versus about $182k for comparable roles.

Based on 240 similar postings.

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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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At a glance

TL;DR · Power Methodology and Modeling Engineer

As a Power Methodology and Modeling Engineer - New College Grad 2026 on the Architecture Energy Modeling Team, you will collaborate with cross-functional engineering teams to study and implement energy modeling techniques for next-generation GPUs, CPUs, and Tegra SOCs. You will develop tools and infrastructure to generate data from post-layout netlists, sanitize metrics for accuracy, and integrate power models with performance tools. Your daily work involves identifying runtime limitations in existing flows, mining pre- and post-silicon performance data to find bottlenecks, and experimenting with machine learning techniques to address design questions. You will utilize Python, C++, PowerBI, and OpenSearch while applying knowledge of VLSI, digital design, computer architecture, and statistical modeling. This role focuses on improving energy efficiency in graphics and AI workloads by providing technical feedback to design teams and optimizing data movement power models.

What you'll do

  • Implement tools and methodologies for generating data from post-layout netlists to feed power analytical models.
  • Develop infrastructure to sanitize metrics in the model to ensure high correlation accuracy.
  • Integrate power models with performance tools through new methodologies and automated processes.
  • Identify and resolve runtime and memory limitations of existing flows to accelerate model delivery.
  • Mine pre- and post-silicon performance data to identify critical data paths and bottlenecks.
  • Provide feedback to design teams to improve the power efficiency of GPUs, CPUs, and SoCs.
  • Experiment with machine learning techniques to answer "what-if" design questions and set energy targets.
  • Enable efficient storage, retrieval, and visualization of data using platforms like PowerBI and OpenSearch.

What we're looking for

  • Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, or equivalent experience.
  • Strong coding skills, preferably in Python and C++.
  • Ability to formulate and analyze algorithms while evaluating runtime and memory complexities.
  • Understanding of VLSI, digital design, and computer architecture concepts.
  • Basic understanding of power and energy consumption estimation and low power design.
  • Basic understanding of the chip design process from RTL design to tape-out.
  • Background in machine learning, AI, and/or statistical modeling is a plus.
  • Ability to use data visualization platforms such as PowerBI or OpenSearch.

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