Intern - Process & Equipment Engineering

Project: Artificial Intelligence Transformation for Process and Equipment Engineering

Location: Singapore

Department: Process and Equipment Engineering

About the Team

Join an inclusive team focused on using expertise in a relentless pursuit of innovation for customers and partners. The solutions we build help enable everything from virtual reality experiences to breakthroughs in neural networks—guided by integrity, sustainability, and giving back to our communities.

Project Description

Artificial Intelligence Transformation for Process and Equipment Engineering aims to enhance engineering productivity, manufacturing excellence, and data-driven decision-making through the application of Artificial Intelligence, automation, and advanced data analytics.

The intern will undertake a defined engineering project exploring how AI-enabled tools and digital workflows can improve how engineers access technical information, analyze manufacturing data, investigate engineering challenges, develop solutions, and identify improvement opportunities across process and equipment domains.

This project provides practical experience in semiconductor manufacturing, engineering analytics, AI-enabled application development, and responsible use of Generative AI. The intern will collaborate with engineers and relevant subject-matter experts while developing and validating a project-based solution.

Objective of the Project

• Identify a suitable process or equipment engineering use case for an AI-enabled solution.
• Develop an application or digital workflow that improves the accessibility, analysis, or interpretation of engineering information.
• Apply Artificial Intelligence, automation, and advanced data analytics to improve problem-solving effectiveness and decision-making.
• Evaluate the proposed solution using defined technical and engineering criteria.

Project Scope

• Study a defined process or equipment engineering challenge and identify opportunities for AI-enabled improvement.
• Explore relevant manufacturing, process, equipment, or engineering datasets and determine suitable analytical approaches.
• Design and develop an AI-enabled application, agentic solution, analytical model, or automated digital workflow.
• Apply appropriate techniques such as statistical analysis, machine learning, Generative AI, large language models, data visualization, or workflow automation.
• Test, validate, document, and present the proposed solution and its potential engineering value.

Learning Opportunities

• Gain practical exposure to process and equipment engineering within a semiconductor manufacturing environment.
• Learn how Artificial Intelligence and advanced data analytics can be applied to engineering and manufacturing challenges.
• Develop experience in data preparation, exploratory analysis, model development, solution validation, and technical documentation.
• Explore responsible use of Generative AI, AI assistants, agentic AI, and large language models in engineering workflows.

Deliverables

• A functional AI-enabled application, analytical model, agentic solution, or automated digital workflow for the selected engineering use case.
• A documented methodology covering the problem statement, data requirements, solution design, testing approach, and validation results.
• A demonstration of how the solution may contribute to yield, quality, cost, productivity, or cycle-time optimization.
• A final technical report and project presentation covering the solution, findings, limitations, and recommendations.

Impact of the Project

The project is intended to demonstrate how AI-enabled engineering approaches can reduce manual analysis, improve information accessibility, accelerate problem-solving, and enhance data-driven decision-making.

The completed solution may identify improvement opportunities relating to yield, quality, cost, process control, equipment effectiveness, or cycle time. Where appropriate, the documented methodology and reusable components may provide a foundation for future engineering analytics and automation projects.

Skillsets Required

• Currently pursuing a degree or equivalent qualification in an engineering, computer science, data science, mathematics, physics, or related technical discipline.
• Familiarity with data analysis, statistics, or programming using tools or languages such as Python, Structured Query Language (SQL), R, or equivalent technologies.
• Familiarity with Artificial Intelligence, machine learning, Generative AI, AI assistants, or other AI-enabled workflows.
• Strong analytical and problem-solving skills, with the ability to translate engineering challenges into structured technical solutions.
• Ability to collaborate with interdisciplinary engineering teams and communicate technical findings clearly.

Course of Interest

The ideal candidate should be pursuing a degree in Chemical Engineering, Mechanical Engineering, Electrical or Electronic Engineering, Materials Science and Engineering, Computer Engineering, Computer Science, Data Science, Mathematics, Physics, or a related engineering or science discipline.

Duration of Period

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

Opportunities for Full-Time Employment

Successful completion of the internship may provide exposure to future graduate employment opportunities, subject to business requirements, position availability, and the applicable selection process.

About Micron Technology, Inc.

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

To learn more, please visit micron.com/careers.