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Micron Technology

Makes memory and storage chips for AI and PCs

NAND Reliability Product Engineer - Stpg PE Nand

Data EngineeringConfirmed live in the last 24 hours
Full-timeSingaporeNo salary listedIn-person

About the Job

Requirements

  • A bachelor’s degree with more than five years of experience or a master’s degree with more than three years of experience in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related discipline.
  • Strong understanding of data analytics, artificial intelligence and machine learning, or related technical fields.
  • Knowledge of semiconductor reliability fundamentals, NAND technology, reliability engineering, and electrical failure analysis methodologies.
  • Strong programming proficiency in Python and SQL.
  • Understanding of data engineering concepts, including data cleansing, outlier treatment, missing data handling, and scalable data processing.
  • Strong analytical, problem-solving, communication, and technical presentation skills.
  • Demonstrated ownership and accountability, and ability to work effectively in cross-functional environments.

Responsibilities

  • Define, assess, and safeguard the intrinsic and extrinsic reliability of NAND Flash products through advanced analytics, large-scale data infrastructure, artificial intelligence, and engineering innovation.
  • Develop scalable data and artificial intelligence solutions that enable faster engineering decisions, improve product quality, and strengthen reliability risk assessment.
  • Drive development and enhancement of cloud-based data platforms that support reliability monitoring, engineering analysis, and product disposition workflows.
  • Design, develop, and maintain scalable extract, transform, and load pipelines for ingesting, validating, transforming, and managing large-scale engineering and manufacturing data from multiple sources.
  • Collaborate with information technology and data platform teams to establish reliable, automated, and sustainable data pipelines.
  • Use Python, SQL, BigQuery, PySpark, and cloud-native services to process large-scale datasets, perform analysis, generate visualizations, and deliver engineering insights.
  • Partner with domain experts and cross-functional teams to translate engineering problems into scalable artificial intelligence, machine learning, and analytics solutions.
  • Develop, evaluate, and deploy artificial intelligence and machine learning solutions for engineering decision-making, failure analysis, anomaly detection, and reliability assessments.
  • Implement data processing techniques, including data cleansing, outlier detection, and missing data handling, using distributed or large-scale frameworks such as PySpark and BigQuery.
  • Support deployment, monitoring, and operationalization of artificial intelligence and machine learning solutions in cloud and enterprise environments.
  • Identify and drive opportunities where generative artificial intelligence, machine learning, and analytics can improve engineering productivity and business outcomes.
  • Integrate AI-assisted tools and data-driven insights to improve productivity, quality, and operational effectiveness.
  • Promote adoption of AI-enabled solutions and contribute to continuous learning, innovation, and operational excellence.
  • Support customer inquiries and provide technical insights related to product reliability.
  • Communicate technical findings, recommendations, and model outcomes to technical and non-technical stakeholders.
  • Integrate AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, using sound judgment and complying with organizational standards and legal requirements.
  • Identify, test, and share AI-enabled improvements within the scope of work.

Desired Qualifications

  • Strong technical foundation in data analytics and visualization, including pandas, scikit-learn, matplotlib, plotly, or similar tools and libraries.
  • Hands-on experience developing and deploying artificial intelligence and machine learning systems involving large language models, including retrieval-augmented generation, agentic workflows, and frameworks such as PyTorch or TensorFlow.
  • Familiarity with modern AI coding tools or agentic coding harnesses such as Claude Code, Roo Code, Cursor, Cline, Windsurf, Gemini CLI, or similar tools.
  • Cloud experience with Google Cloud Platform, Amazon Web Services, or Microsoft Azure, including deploying machine learning pipelines in production.
  • Experience analyzing large, complex, heterogeneous datasets from multiple systems and applying techniques for data cleansing, outlier handling, and missing data treatment.

About the Company

Micron Technology

Makes memory and storage chips for AI and PCs

Micron Technology designs and manufactures memory and storage chips, mainly DRAM and NAND flash, used in data centers, PCs, phones, cars and industrial equipment. It is one of only three companies that make HBM, the stacked high-bandwidth memory placed beside processors in AI servers, and it is the largest memory chipmaker based in the United States. Unlike chip designers that outsource production, Micron runs its own fabs and assembly sites in the United States and across Asia. It earns money by selling chips, memory modules and solid-state drives to cloud providers, device makers and distributors. Micron was founded in 1978 and is headquartered in Boise, Idaho.

Company size
10,001+
Company stage
IPO
Headquarters
Boise, ID
Founded
1978

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