Power Integrity AI Engineer – PDN Optimization & Design

1000 Micron Technology, Inc.; San Jose (CA); On-site; USD 170,000 - 290,000; Full time

Job summary

Micron Technology, Inc. in the United States is seeking a power integrity and AI-enabled engineer to embed AI into PDN analysis, optimize power grids, and accelerate PDN design-space exploration.

You will collaborate with design, CAD, package, and product teams, applying data-driven methods to predict IR drop and electromigration risk while driving AI-assisted optimization of decap placement and PDN co-design.

Qualifications

• Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent practical experience.
• 5 to 7 years of experience in power integrity, PDN design, circuit design, physical design, package design, or EDA workflows.
• Strong coding skills with Python or similar languages; data-driven approach to engineering workflows.

Responsibilities

• Embed AI and automation into PDN analysis, power integrity validation, and optimization workflows.
• Apply data-driven methods to predict IR drop, voltage droop, current density, decap placement and EM risk.
• Enable AI-assisted optimization of power grid architectures and package-PDN co-design.
• Leverage historical design, simulation, and silicon data to drive predictive insights and recommendations.
• Develop machine learning solutions to automate root-cause analysis and accelerate PDN exploration.
• Collaborate across power integrity, design, CAD, package, and product engineering teams.

Skills

Python programming

Data analysis

ML in engineering workflows

Collaboration across teams

Education

Bachelors in EE/CE/CS

Tools

EDA tools

Job description

Micron Technology, Inc. in the United States is seeking a power integrity and AI-enabled engineer to embed AI into PDN analysis, optimize power grids, and accelerate PDN design-space exploration.

You will collaborate with design, CAD, package, and product teams, applying data-driven methods to predict IR drop and electromigration risk while driving AI-assisted optimization of decap placement and PDN co-design.