SeniorStaff Product Engineer (GenAI, AIML & 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 learncommunicateand 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 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 DesignEngineeringSystem EngineeringData 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 andoptimizeLLM-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 andmissing-datahandling using distributed or large-scale frameworks (e.g.PySparkBigQuery).

• 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:Identifyand drive high-impact opportunities where GenAI machine learning and analytics can improve engineering productivity and business outcomes.

Minimum Qualifications

• BachelorsorMasters degree inElectricalEngineering Computer Science Data Science Statistics Artificial Intelligence ora relatedfield.

• Minimum 2 years of hands-on experience developing and deploying AI applications insemiconductors electronics orother engineeringindustries.

• Strong programmingproficiencyin Python and SQL.

• Strong technical foundation in data analytics and visualization including tools and libraries such as pandas scikit-learn matplotlibplotly or similar ecosystems.

• Familiarity with agentic AI frameworks such asLangGraph Google ADKAutoGenand evaluation tools likeAgentEval.

• 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 agenticworkflows and frameworks such asPyTorchor TensorFlow.

• Cloud experience with GCP AWS or Azure including deploying ML pipelines in production.

• Experience with LLM training inference andevaluationworkflows 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 andmissing-datatreatment.

• Strong analytical problem-solving and software development skills.

• Strong communicationskills with the ability to explain technical concepts and findings effectively.

• Strong senseof ownership accountability and engineering rigor.

Preferred Qualifications

• Experience building agentic systems or AI solutions for semiconductor manufacturing product engineering validation yield improvement reliability or failure analysis.

• Deep understanding of semiconductor-specific AI/ML applications

• Demonstrated understanding of deep learning architectures and computer vision.

• Experience using enterprise data platforms such asBigQuery SnowflakeMSSQL Oracle or Redshift.

• Experience with Kubernetes or similar production infrastructure and deployment frameworks.

• Experience with web application technologies such as JavaScript HTML and CSS.

• Experience designing scalable enterprise-grade AI/ML systems with attention to reliability traceability and operational readiness.

• Familiarity with document-processing pipelines technical knowledge platforms or retrieval systems for engineering content.

• Experience working in cross-functional environments spanning engineering manufacturing data science and IT.