Intern - Product Development Engineer

Micron Singapore Bendemeer — MSB Test Solutions Engineering

Project Title: NAND Component Test Flow Optimization Using AI-Enabled Data Analytics and Automation

Location: Micron Singapore Bendemeer (990 Bendemeer Road, Singapore 339942)

About the Role

Join an inclusive team passionate about using expertise in the pursuit of innovation for customers and partners. The solutions we build enable everything from virtual reality experiences to breakthroughs in neural networks.

Project Description

The intern will participate in a structured Product Development Engineering project focused on optimizing NAND component test flows using manufacturing data analytics, automation, and Artificial Intelligence.

Under appropriate guidance, the intern will develop analytical tools, models, and visualizations to study manufacturing data, identify product recovery opportunities, and improve test-flow efficiency. The project provides practical exposure to data engineering, statistical analysis, machine learning, prompt engineering, and the responsible application of enterprise AI tools.

Objective of the Project

The project aims to:

• Improve the analysis of manufacturing and test-flow data.
• Identify data-driven opportunities to enhance product recovery.
• Develop automation solutions that improve engineering analytical efficiency.
• Apply statistical, machine learning, and AI-enabled methodologies to generate actionable insights.
• Evaluate emerging Artificial Intelligence technologies for relevant engineering and manufacturing use cases.

Project Scope

The intern will have the opportunity to:

• Retrieve, prepare, analyze, and visualize manufacturing data to evaluate NAND component test-flow efficiency and product recovery opportunities.
• Work with SpecTek engineers to understand test-flow challenges and apply structured, data-driven problem-solving methodologies.
• Develop scripts, analytical models, reports, and automation solutions using relevant programming languages, statistical tools, and data platforms.
• Apply statistical analysis, machine learning, enterprise AI tools, and prompt-engineering techniques to accelerate data exploration, knowledge retrieval, documentation, and insight generation.
• Evaluate AI-enabled solutions for engineering use cases, validate AI-generated outputs, and follow applicable data security, governance, and responsible AI requirements.

Learning Opportunities

The intern will have the opportunity to:

• Gain practical exposure to NAND component testing, product recovery, and manufacturing data analysis.
• Develop proficiency in programming, data preparation, statistical analysis, machine learning, automation, and visualization.
• Learn how AI-enabled tools and prompt-engineering techniques can be applied responsibly within engineering workflows.
• Build experience in validating analytical and AI-generated outputs, documenting assumptions, and communicating limitations.
• Strengthen critical thinking, technical documentation, stakeholder communication, and cross-functional collaboration skills.

Deliverables

The intern is expected to deliver:

• A structured analysis of NAND component manufacturing and test-flow data.
• Scripts, analytical models, or automation tools for data preparation, analysis, and reporting.
• Statistical or machine learning analyses identifying potential product recovery and test-flow improvement opportunities.
• Visualizations and documented recommendations for engineering evaluation.
• A final technical report and presentation covering the methodology, findings, validation results, limitations, and proposed next steps.

Impact of the Project

The project is intended to:

• Improve visibility into NAND component test-flow performance and product recovery opportunities.
• Increase the efficiency and consistency of engineering data analysis.
• Enable data-driven evaluation of potential test-flow improvements.
• Strengthen the responsible use of automation and AI-enabled analytics in engineering workflows.
• Establish reusable analytical methods and tools for future engineering projects.

Skillsets Required

The ideal candidate should possess:

• Programming knowledge in Python, C++, SQL, or equivalent analytical languages.
• Familiarity with data analysis libraries and platforms such as Pandas, Perl, Hadoop, JMP, or equivalent tools.
• Coursework or project experience in Artificial Intelligence, machine learning, data engineering, statistical analysis, or related areas.
• Familiarity with AI-enabled tools, coding assistants, prompt engineering, or data analytics platforms, including the validation of AI-generated outputs.
• Strong analytical, critical-thinking, problem-solving, collaboration, communication, and technical documentation skills.

Course of Interest

The ideal candidate should be pursuing a bachelor’s or master’s degree in Computer Science, Data Science, Electrical Engineering, Computer Engineering, Artificial Intelligence, Machine Learning, or a related field.

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.