AI-Enabled NAND Device Engineering Intern

Micron Technology, Inc; Singapore; On-site; SGD 22,000 - 33,000; Full time

Benefits offered by this job

Mentorship

Learning opportunities

Job summary

Micron Technology, Inc. in Singapore is seeking an AI/Data Science Intern in NAND Device Engineering to develop AI-enabled workflows for device characterization data and automation.

You will build data pipelines, dashboards, and ML models, collaborating with engineers to accelerate technology development. You will apply Python-based data science, ML, and analytics techniques to large semiconductor datasets, contributing to AI-enabled decision-support tools and engineering productivity

Qualifications

• Proficiency in Python for data analysis and automation.
• Experience with ML/AI concepts and related coursework or projects.
• Familiarity with data processing libraries and visualization tools.
• Strong analytical thinking, problem-solving, and communication skills.

Responsibilities

• Design AI-enabled workflows for device characterization data collection and analysis.
• Develop intelligent dashboards and visualization tools for engineers.
• Build automated data pipelines from characterization and metrology sources.
• Apply ML/Agentic AI techniques to identify performance trends and generate insights.
• Collaborate with cross-functional teams to accelerate technology development.

Skills

Python

Machine learning

Data analysis

Data visualization

Problem solving

Communication

Education

Bachelor's degree in CS/CE/EE

Pursuing Master/PhD

Tools

TensorFlow

PyTorch

Pandas

NumPy

Scikit-learn

LangChain

Dashboard tools

SQL

Job description

Micron Technology, Inc. in Singapore is seeking an AI/Data Science Intern in NAND Device Engineering to develop AI-enabled workflows for device characterization data and automation.

You will build data pipelines, dashboards, and ML models, collaborating with engineers to accelerate technology development. You will apply Python-based data science, ML, and analytics techniques to large semiconductor datasets, contributing to AI-enabled decision-support tools and engineering productivity