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
Employment
Full-time
Posted
175 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $184k
This role $153k
$103k $238k
below market most similar roles pay here above market

This role pays less than 68% of similar roles. Most pay $151,624–$216,250 — the blue band above. At the midpoint, this role pays about $153k versus about $184k 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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 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, you will join the Architecture Energy Modeling Team to study and implement energy modeling techniques for next generation GPUs, CPUs, and Tegra SOCs. You will define and implement tools for efficient data generation from post layout netlists, develop infrastructure to sanitize metrics for high correlation accuracy, and integrate power models with performance tools. Your daily work involves mining data from pre- and post-silicon performance runs to identify bottlenecks, providing feedback to design teams, and experimenting with machine learning techniques to set power targets. You will utilize Python, C++, PowerBI, and OpenSearch while applying knowledge of VLSI, digital design, computer architecture, and statistical modeling to improve energy efficiency in graphics and AI workloads.

What you'll do

  • Define and implement tools for generating data from post-layout netlists for power analytical models.
  • Develop infrastructure to sanitize model metrics and ensure high correlation accuracy.
  • Create methodologies to integrate power models with existing performance tools.
  • Identify runtime and memory limitations in current flows to accelerate model delivery.
  • Mine data from pre- and post-silicon performance runs to identify data paths and bottlenecks.
  • Experiment with machine learning techniques to answer design questions and set power targets.
  • Enable efficient data storage, retrieval, and visualization using platforms like PowerBI and OpenSearch.

What we're looking for

  • Pursue or recently complete a MS or PhD in Electrical or Computer Engineering or equivalent experience.
  • Possess strong coding skills, preferably in Python and C++.
  • Ability to formulate and analyze algorithms while commenting on runtime and memory complexities.
  • Understand VLSI, digital design, and computer architecture concepts.
  • Possess a basic understanding of power and energy consumption, estimation, and low power design.
  • Possess a basic understanding of the chip design process from RTL design to tape-out.
  • Background in machine learning, AI, and/or statistical modeling (preferred).
  • Desire to bring quantitative decision-making and analytics to improve product energy efficiency.

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