AI-Driven NAND Device Engineering Intern

Micron Memory Malaysia Sdn Bhd

Singapore

On-site

SGD 13,392 - 25,668

Part time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Micron Technology, Inc. invites an AI/Data Science Intern to join the NAND Device Engineering team in Singapore.

You will design AI-powered automation for electrical characterization of 3D NAND memory cells, build end-to-end data pipelines, and create decision-support tools to speed device optimization and DOE planning. You will apply ML/AI techniques, develop automated workflows, and deliver dashboards and reports.

Qualifications

Currently pursuing a Bachelor's degree / Master’s degree / Ph.D in Computer Science, Data Science, AI, Statistics, Electrical Engineering, Physics, Mathematics, or a related field, and available preferably for a semester-long internship.
Strong proficiency in Python and experience building end-to-end data analysis or automation workflows is required.
Demonstrated experience or coursework in machine learning, deep learning, or AI applications (scikit-learn, TensorFlow, PyTorch, LangChain, or similar frameworks) is required.
Experience with automation and workflow orchestration (e.g., scripting pipelines, API integrations, Power Automate, Airflow, or similar tools) is highly preferred.
Familiarity with LLMs, NLP, or agentic AI frameworks for building intelligent assistants or automated workflows is a strong plus.
Experience with SQL, data visualization tools (Tableau, Power BI, Streamlit), or dashboard development is a plus.
Exposure to semiconductor device physics, NAND flash memory, or electrical characterization is a plus.
Experience with cloud platforms (Azure, AWS, GCP) or big data tools (Spark, BigQuery) is a bonus.

Responsibilities

Design and develop AI-powered automation workflows for electrical characterization of 3D NAND memory cells, including automated data extraction, analysis, reporting, and anomaly flagging across program, read, erase, reliability, and variability datasets.
Build intelligent decision-support tools that synthesize large-scale device data into prioritized, actionable recommendations — enabling faster engineering decisions on device optimization, design-of-experiment planning, and risk assessment.
Develop AI-driven task and project management solutions that automate tracking, prioritization, status reporting, and follow-up across device engineering projects and cross-functional workstreams.
Apply machine learning techniques (classification, regression, clustering, NLP, agentic AI) to automate pattern recognition, trend identification, and root-cause analysis in device performance datasets.
Create automated data pipelines for data extraction, cleaning, feature engineering, and integration across multiple characterization and metrology data sources.
Build interactive visualizations, dashboards, and auto-generated summary reports that communicate device insights to technical and non-technical audiences with minimal manual effort.
Explore and implement LLM-based and agentic AI approaches for automating routine engineering workflows such as data interpretation, report generation, meeting summarization, and knowledge retrieval.
Work independently with a high level of self-motivation, time management, and prioritization skills.
Deliver a complete intern project package, including code/notebooks, automation tools, documentation, and presentation-ready summaries.