Architecture Energy Modeling Engineer

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$116,000–$189,750 / yr
Posted
21 days ago
Freshness
Confirmed live yesterday

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $185k
This role $153k
$103k most similar roles pay here $238k

This role pays less than 64% of similar roles. Most pay $145,975–$225,000 — the shaded band above. At the midpoint, this role pays about $153k versus about $185k 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 · Architecture Energy Modeling Engineer

Architecture Energy Modeling Engineer - New College Grad 2026 will join the Power Modeling, Methodology and Analysis Team to develop methodologies for making products more energy efficient. The role involves building Machine Learning based power models to analyze and reduce consumption of GPUs, CPUs, and Tegra SOCs. You will collaborate with architects and engineers to study modeling techniques, improve model accuracy using statistical methods, and estimate data movement power. Key tasks include integrating models into architectural simulators and RTL platforms, debugging energy inefficiencies on silicon, and prototyping new features to improve performance per watt. Required skills include Python, C++, machine learning, and statistical modeling expertise. Candidates should have a background in computer architecture, algorithm analysis, and low power design principles. Familiarity with Verilog and ASIC design principles is also considered an advantage for this role.

What you'll do

  • Develop Machine Learning based power models to analyze and reduce energy consumption of GPUs.
  • Create methodologies and workflows to train models using ML and statistical techniques.
  • Improve model accuracy by experimenting with different representations, objective functions, and learning algorithms.
  • Develop methods to accurately estimate the power and energy costs of data movement.
  • Correlate predicted energy from early design stages with actual silicon measurements.
  • Integrate power and energy models into performance infrastructure platforms for combined reporting.
  • Build tools to debug and resolve energy inefficiencies observed in silicon, RTL, and architectural simulators.
  • Prototype new architectural features and analyze their impact on system energy efficiency.

What we're looking for

  • Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
  • Strong coding skills, preferably in Python and C++.
  • Background in machine learning, AI, and/or statistical modeling.
  • Background in computer architecture and interest in energy-efficient GPU designs.
  • Ability to formulate and analyze algorithms while commenting on runtime and memory complexities.
  • Basic understanding of fundamental concepts of energy consumption, estimation, and low power design.
  • Good verbal, written, and interpersonal communication skills.
  • Familiarity with Verilog and ASIC design principles (preferred).

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