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Location

Singapore

Department

Product Engineering

Project Title

Machine Learning and Agentic AI Solutions for Semiconductor Product Engineering

Project Description

The Product Engineering Machine Learning and Agentic AI Intern will undertake an engineering project focused on applying Artificial Intelligence to semiconductor yield analysis, test optimization, data processing, and workflow automation.

Working under the guidance of experienced Product Engineers and Citizen Data Scientist mentors, the intern will develop Machine Learning models, explore Agentic AI solutions, and evaluate engineering data from probe, wafer, test, and manufacturing processes.

The project provides hands-on exposure to structured data analytics, Artificial Intelligence agents, engineering automation, and production-oriented AI applications in a semiconductor environment.

Objective of the Project

Develop Machine Learning solutions for semiconductor yield, reliability, test-time, or cycle-time improvement.
Explore Agentic AI applications that automate selected engineering analysis and documentation workflows.
Transform high-volume semiconductor datasets into structured, analysis-ready information.
Evaluate potential applications of Artificial Intelligence that improve engineering efficiency and decision-making.
Build practical knowledge relevant to future Product Engineering and Citizen Data Scientist roles.

Opportunities for Full Time Employment

Interns may be considered for future internship or full-time employment opportunities based on business needs, role availability, academic completion, and demonstrated capabilities.

Project Scope

Develop and evaluate predictive models for yield, product reliability, test-time optimization, or semiconductor manufacturing analytics.
Apply regression, decision-tree, ensemble-learning, and boosting techniques to structured probe, wafer, test, and manufacturing datasets.
Design Agentic AI prototypes for engineering use cases such as report generation, anomaly detection, data processing, and test-program analysis.
Prepare, clean, transform, and integrate engineering data for Machine Learning and Artificial Intelligence workflows.
Explore integration concepts involving engineering platforms and enterprise tools such as Jira, Confluence, and SharePoint.

Learning Opportunities

Gain hands-on experience applying Machine Learning to semiconductor Product Engineering challenges.
Learn feature engineering, model evaluation, validation, and interpretation techniques for structured engineering data.
Understand how Artificial Intelligence agents and Large Language Models can be applied to engineering automation.
Learn how engineering data is collected, transformed, governed, and used within AI-Enabled workflows.
Participate in technical learning activities guided by Product Engineers, Artificial Intelligence specialists, and Citizen Data Scientist mentors.

Deliverables

A Machine Learning model or analytical methodology for a selected yield, reliability, testing, or cycle-time use case.
An Agentic AI prototype that demonstrates automation of a defined engineering workflow.
A structured data preparation and feature-engineering pipeline for the selected project dataset.
Technical documentation covering the project approach, model evaluation, limitations, and recommended next steps.
A final demonstration and presentation communicating the project findings and potential engineering applications.

Impact of the Project

Improve the visibility of semiconductor yield, reliability, and product-test patterns.
Identify opportunities to reduce engineering analysis time and accelerate technical learning.
Demonstrate practical applications of Artificial Intelligence and Agentic AI within Product Engineering.
Contribute reusable analytical methods, automation concepts, or best-practice documentation for future engineering projects.

Skillsets Required

Basic programming knowledge in Python and familiarity with libraries such as Pandas, NumPy, or Scikit-learn.
Understanding of Machine Learning concepts such as regression, decision trees, feature engineering, model validation, and performance evaluation.
Interest in Artificial Intelligence agents, Large Language Models, workflow automation, or AI-Enabled engineering solutions.
Strong analytical thinking, problem-solving ability, curiosity, and willingness to learn.
Clear written and verbal communication skills, with the ability to collaborate in a cross-functional engineering environment.

Preferred Qualifications

Coursework or project experience in Machine Learning, data science, data engineering, Artificial Intelligence, or automation.
Exposure to TensorFlow or PyTorch for image, text, or engineering log analysis.
Awareness of Agentic AI frameworks or platforms such as LangChain or Microsoft Copilot Studio.
Basic understanding of Machine Learning Operations, including model lifecycle, evaluation, deployment, and monitoring.
Interest in cloud-based Artificial Intelligence technologies, including Microsoft Azure or Amazon Web Services.

Course of Interest

The ideal candidate should be pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, Electronics Engineering, Computer Engineering, Data Science, Data Analytics, Artificial Intelligence, or a related technical field.

Duration of Period

The ideal candidate should be able to commit to a full time internship period of at least 5 months from Jan to May 2027.

About Micron Technology, Inc.

We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.

To learn more, please visit micron.com/careers

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

To request assistance with the application process and/or for reasonable accommodations, please contact [HIDDEN TEXT]

Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.

Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.

AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.

Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.

Intern - NAND Product Engineering - Probe AI/ML