Intern - CDM

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.

Project Title

AI-Enabled Quality Engineering Transformation for Semiconductor Manufacturing

Project Description

Develop quality engineering expertise in semiconductor manufacturing through analytical problem-solving, statistical analysis, quality systems, process characterization, and risk management. The intern will undertake a structured AI-Enabled transformation project to improve productivity, workflow efficiency, data visibility, and engineering decision-making for contamination control, incoming quality control, and/or chemical laboratory analysis. The project will provide practical exposure to industrial data analytics, Artificial Intelligence, automation, visualization, and cross-functional engineering collaboration.

Objective of the Project

• Analyze a defined quality engineering workflow and identify opportunities to improve productivity, efficiency, and decision-making.
• Develop an AI-Enabled, automation, or digital solution addressing a real engineering challenge.
• Evaluate the solution using appropriate technical, quality, usability, and business-impact criteria.
• Build practical knowledge of quality systems, statistical methods, process characterization, and risk management in semiconductor manufacturing.

Opportunities for Full Time Employment

The internship provides exposure to quality engineering career opportunities within semiconductor manufacturing. Interns may be considered for future internships or full-time employment based on business requirements, role availability, project outcomes, and the applicable recruitment process.

Project Scope

• Analyze engineering workflows within contamination control, incoming quality control, and/or chemical laboratory analysis.
• Develop digital solutions that enhance data visibility, reporting efficiency, and engineering decision-making.
• Apply Generative AI, Large Language Models, AI Agents, machine learning, and statistical techniques to enhance inspection strategies, automate quality-data analysis, or improve anomaly detection.
• Develop and evaluate a prototype, dashboard, analytical model, Agentic Solution, or automated workflow for the selected use case.

Learning Opportunities

• Gain exposure to quality engineering systems and practices within a semiconductor manufacturing environment.
• Learn statistical analysis, structured problem-solving, process characterization, and risk-assessment methodologies.
• Explore AI-Enabled workflows, Generative AI, AI Agents, data visualization, and software-based or physical automation technologies.
• Collaborate with cross-functional engineering teams and subject matter experts.
• The internal quality-project example similarly emphasizes semiconductor manufacturing analytics, machine learning, predictive modelling, visualization, and cross-functional collaboration.

Deliverables

• An AI-Enabled, automation, or digital transformation solution addressing a defined quality engineering challenge.
• Evaluation results demonstrating solution effectiveness, identified limitations, and potential business impact.
• Technical documentation covering the methodology, solution design, data requirements, risks, and recommendations.
• A final project report and leadership presentation covering the problem statement, methodology, results, business impact, and future scaling opportunities.
• The internal internship programme requires interns to present project deliverables to department leaders and includes final reports and presentations within the completion documentation.

Impact of the Project

• Improve the visibility, consistency, and accessibility of relevant quality engineering information.
• Reduce inefficient or repetitive steps in the selected engineering workflow.
• Strengthen anomaly identification, engineering reporting, and data-informed decision-making.
• Demonstrate a scalable digital or AI-Enabled approach for future quality engineering transformation.

Skillsets Required

• Strong analytical, statistical, and structured problem-solving skills.
• Familiarity with Python, R, Structured Query Language, data visualization, or equivalent analytical tools.
• Familiarity with machine learning, Generative AI, Large Language Models,