Senior/Staff Product Engineer (Genai, AI/ML & Advanced Data Analytics)

Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As part of the HIG HBM Product Engineering organization, you will help drive the development of next-generation GenAI, machine learning, and advanced data analytics solutions for semiconductor engineering. In this role, you will work on intelligent systems that improve engineering productivity, strengthen technical decision-making, and unlock insights from complex manufacturing, validation, and engineering workflows. You will collaborate with cross-functional teams across Product Engineering, Design Engineering, System Engineering, Data Science, IT, and Manufacturing to prototype, build, and scale practical AI-driven solutions that improve quality, cost, cycle time, and engineering efficiency. Key Responsibilities GenAI System Development: 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. Large-Scale Data Pipelines: Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems. Advanced Data Analytics: Apply Python, SQL, and data science libraries (e.g., pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights. LLM Workflow Engineering: Build, evaluate, and optimize LLM-based workflows, including prompting, retrieval-augmented generation (RAG), inference orchestration, benchmarking, and quality evaluation. Machine Learning Production: Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support. Distributed Data Processing: 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). Cross-Functional Collaboration: Partner with domain experts and cross-functional teams to translate complex engineering problems into scalable AI/ML and analytics solutions. Production Deployment: Support deployment, monitoring, and operationalization of AI/ML solutions in cloud and enterprise environments. Technical Communication: Communicate technical findings, recommendations, and model outcomes clearly to both technical and non-technical stakeholders. Innovation Leadership: Identify and drive high-impact opportunities where GenAI, machine learning, and analytics can improve engineering productivity and business outcomes. Minimum Qualifications 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. Hands-on experience developing and deploying AI/ML systems involving LLMs, including RAG, agentic workflows, and frameworks such as PyTorch or TensorFlow. Cloud experience with GCP, AWS, or Azure, including deploying ML pipelines in production. Experience with LLM training, inference, and evaluation workflows, including prompt design, benchmarking, validation, or retrieval-augmented systems. Experience analyzing large, complex, and heterogeneous datasets from multiple systems and applying sound techniques for data cleansing, outlier handling, and missing-data treatment. Strong analytical, problem-solving, and software development skills. Strong communication skills with the ability to explain technical concepts and findings effectively…