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# ASIC Gen-AI Data Scientist

- **Company:** Micron Technology, Inc.
- **Location:** Folsom, CA, United States
- **Experience:** Experienced
- **Salary:** $145,000.0 - $336,000.0
- **Contract:** Permanent contract
- **Skills:** HTML, JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Automated Storage and Retrieval Systems, Microsoft Azure, Cascading Style Sheets (CSS), Cloud Computing, Code Generation, Computer Programming, Data Mining, Cursor, Decision Support Systems, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Software Deployment, Systems Architecture, Web Applications, Application Specific Integrated Circuits, Retrieval-Augmented Generation, Large Language Models, Claude Code, Prompt Engineering, Generative AI, Agentic-AI, Pandas, Matplotlib, Scikit Learn, Information Technology, Plotly, Machine Learning Operations, Google Gemini, Graph RAG, Data Pipelines
- **Published:** October 23, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3466352012&tx=IT8679TTT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's with 10+ years or Master's degree with 8+ in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field
 * Minimum 4 years of hands-on experience developing and deploying AI applications in semiconductors, electronics, or other engineering industries.
 * Strong programming proficiency in Python.
 * Familiarity with modern AI coding tools / agentic coding harnesses, such as Claude Code, Cursor, Gemini CLI, or similar tools.
 * Practical experience in developing and implementing AI/ML systems involving LLMs, including RAG, agentic workflows, and frameworks., * Experience building agentic systems or AI solutions for ASIC semiconductor pre-silicon and post-silicon workflows.
 * Deep understanding of semiconductor-specific AI/ML applications
 * 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 with data analytics and visualization to deliver actionable engineering insights using tools and libraries such as pandas, scikit-learn, matplotlib, plotly, or similar ecosystems.
 * 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 multi-functional environments spanning systems architecture, engineering, data science, and IT.

## Description

* GenAI System Development: Design, build, and improve GenAI-powered and agentic systems supporting ASIC semiconductor engineering workflows such as code generation, data extraction, analytics, documentation automation, failure triage, root cause analysis, and technical knowledge retrieval.
 * LLM Workflow Engineering: Build, evaluate, and optimize LLM-based workflows, including prompting, developing skills, retrieval-augmented generation (RAG and Graph-RAG), benchmarking, and quality evaluation.
 * Machine Learning Production: Develop and implement machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.
 * 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!