Engineer, STPG PE NAND

Job Summary:

Join Product Engineering as a pathfinding engineer for machine learning and agentic AI. You will evaluate emerging technologies, build and benchmark prototypes, and partner with semiconductor subject-matter experts to convert proven concepts into secure, scalable production tools. The role covers predictive ML, RAG and knowledge graphs, AI agents, cloud deployment, and enablement of Citizen Data Scientists to improve yield, test time, cost, cycle time, and quality. 

Main Responsibilities:

• Evaluate emerging ML and agentic AI research, reproduce promising approaches on internal data, and use rigorous benchmarks to guide technology decisions.
• Build predictive ML solutions for yield, test time, reliability, and manufacturing analytics using high-volume probe, WAT, STDF, and wafer-fab data.
• Design RAG and GraphRAG solutions, including document ingestion, ontology and schema design, hybrid retrieval, and measurable evaluation against baseline retrieval.
• Develop AI agents and multi-agent workflows for engineering automation, including validation, yield and failure analysis, and report generation.
• Create and maintain reusable TASKAI skills, plugins, sub-agents, hooks, MCP servers, quality gates, search, audit, and usage tooling.
• Integrate AI solutions with enterprise platforms such as Jira, Confluence, SharePoint, Outlook, Teams, and Bitbucket.
• Partner with test, yield, product, reliability, architecture, and security teams to productionize solutions with observability, evaluation, cost control, and governance.
• Guide Citizen Data Scientists through structured training, mentoring, technical standards, and review of ML and agentic AI initiatives.
• Present findings and data-driven recommendations to technical teams and management; contribute to standards, technical papers, patents, and reusable guidance.

Other Responsibilities:

• Build reliable data and document-ingestion pipelines that preserve tables, metadata, and structure for ML and retrieval use cases.
• Identify opportunities to reduce test time, improve yield, and automate Product Engineering workflows.
• Maintain clear documentation, code-review practices, and continuous improvement of shared platforms and tools.

AI Responsibilities: 

• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements
• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.

Minimum Required Qualifications/Experience

• Master's degree in Computer Science, Electrical/Electronic Engineering, Data Science, Artificial Intelligence,  
• Strong proficiency in Python and data processing libraries (e.g., Pandas, NumPy, Scikit-learn) 

• Experience with machine learning algorithms for structured data, including regression techniques, tree-based models, and gradient boosting frameworks 

• Ability to build and evaluate ML models for predictive analytics, including sound experimental design and statistical testing 

• Information retrieval fundamentals: embeddings, lexical similarity (e.g., Jaccard/BM25), hybrid ranking, and retrieval metrics (Recall@k, R@1, MRR) 

Must Have Technical Skills:

• Hands-on knowledge of RAG, GraphRAG, knowledge graphs, ontology design, and graph or vector retrieval.
• Agentic AI architecture, including tool use, orchestration, memory, human-in-the-loop approvals, least-privilege access, and MCP-based integrations.
• Evaluation and observability practices using benchmark suites, tracing, automated scoring, LLM-as-judge with human calibration, and feedback loops.

Must Have Soft Skills

• Strong problem-solving and analytical thinking; willing to let the data overturn earlier conclusions 

• Outspoken and confident communicator: presents to the team and management, writes clear weekly updates, and challenges assumptions constructively 

• Excellent communication and ability to work in cross-functional teams with semiconductor subject-matter experts for technical exchange and management updates 

• Self-driven pathfinder who can move from paper to prototype to adopted tool with minimal direction 

Highly Desirable/Preferred skills or experience

• Semiconductor industry or manufacturing analytics background (probe, test, yield, STDF, WAT) 

• Hands-on experience with AI agents, Claude Code skill/plugin authoring, or workflow automation 

• Hands-on experience building knowledge graphs or GraphRAG on enterprise documents 

• Knowledge of enterprise integration tools (Power Automate, SharePoint connectors, Microsoft Teams bots)
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) for processing unstructured data (images, text, logs) and scalable model creation and deployment 

• Knowledge of MLOps practices, including model lifecycle management, CI/CD for ML, and production inference workflows for robust deployment 

• Software engineering hygiene: Git/Bitbucket, code review, automated quality gates, documentation in Confluence 

• Web front-ends and APIs for internal tools (e.g., FastAPI/Flask, HTML/JS dashboards) 

Highly Desirable/Preferred skills or experience

• Cloud deployment using AWS or Azure, including containers, storage, identity and access management, monitoring, security, and cost controls.
• Software engineering practices using Git or Bitbucket, code review, CI/CD, automated quality gates, APIs, and technical documentation.

Embody Micron’s core values:

• People – Respect, develop, and empower others
• Innovation – Drive continuous improvement and breakthrough thinking
• Tenacity – Show grit and determination
• Collaboration – Build trust and foster teamwork
• Customer Focus – Deliver excellence and value
• Speed – Act with purpose and set the pace