Intern - NAND Product Engineering - Probe AI/ML

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. 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 ab…