About the role
Micron Technology seeks a Senior/Staff Product Engineer to drive GenAI, AI/ML, and advanced data analytics solutions for semiconductor engineering. The role involves developing intelligent systems to improve engineering productivity, decision-making, and insights from complex manufacturing and validation data. Responsibilities include building GenAI systems, data pipelines, LLM workflows, machine learning models, and collaborating with cross-functional teams.
IDMOnsiteHIG
Key Responsibilities
- Design, build, and improve GenAI-powered and agentic systems supporting semiconductor engineering workflows such as code generation, data extraction, analytics, documentation automation, failure triage, and technical knowledge retrieval.
- Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems.
- Apply Python, SQL, and data science libraries (e.g., pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights.
- Build, evaluate, and optimize LLM-based workflows, including prompting, retrieval-augmented generation (RAG), inference orchestration, benchmarking, and quality evaluation.
- Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.
- Implement robust data processing techniques such as data cleansing, outlier detection, and missing-data handling using distributed or large-scale frameworks (e.g., PySpark, BigQuery).
Requirements
- Bachelor's or Master's degree in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field.
- Minimum 2 years of hands-on experience developing and deploying AI applications in semiconductors, electronics, or other engineering industries.
- Strong programming proficiency in Python and SQL.
- Strong technical foundation in data analytics and visualization, including tools and libraries such as pandas, scikit-learn, matplotlib, plotly, or similar ecosystems.
- Familiarity with agentic AI frameworks such as LangGraph, Google ADK, AutoGen and evaluation tools like AgentEval.
- Familiarity with modern AI coding tools / agentic coding harnesses, such as Claude Code, Roo Code, Cursor, Cline, Windsurf, Gemini CLI, or similar tools.