Staff Engineer, Technology - AMAIS

Req ID:

JR116158 Staff Engineer, Technology - AMAIS

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.

The Advanced Modeling & AI Solutions team is seeking a Sr/Staff Technology-AMAIS Engineer to develop and deploy physics and data-driven process models, composite modeling solutions, and scalable modeling workflows within our Virtual Process Modeling (VPM) and SPOCK program using Machine Learning, Generative AI, and Agentic AI solutions.

The engineer will work at the intersection of semiconductor process/integration engineering, Artificial Intelligence, Large Language Models (LLMs), predictive modeling, statistical learning, and AI-enabled automation to develop predictive and prescriptive models for advanced DRAM, HBM, and technology-transfer applications. The role will also contribute to scaling VPM through automation and SPOCK capabilities. Current FY27 SPOCK work includes VPM composite-models, UC7, VPM UI ecosystem, MIND/App Hub, and agentic orchestration.

Key Responsibilities include:

• Develop and deploy VPM process models using semiconductor manufacturing, equipment, metrology, wafer, and product data to optimize process performance.
• Apply advanced analytics, statistical methods, AI, and Agentic AI to solve complex semiconductor technology and manufacturing challenges.
• Build and enhance AI-enabled SPOCK/composite modeling workflows that integrate process data, machine learning algorithms, statistical models, and engineering expertise.
• Support the full model lifecycle, including development, validation, deployment, monitoring, and refresh of VPM and SPOCK applications.
• Create scalable automation and workflow solutions to improve modeling efficiency, robustness, and repeatability.
• Evaluate model performance through statistical validation, robustness testing, and detailed analysis to ensure fitness for use.
• Translate modeling insights into actionable recommendations that drive process optimization, yield improvement, technology development, and manufacturing excellence.
• Collaborate with multi-functional and global teams to scope projects, establish standard processes, maintain documentation/governance, and communicate technical results to partners and leadership.

Required Qualifications:

• Master's or Ph.D. in Electrical Engineering, Chemical Engineering, Mechanical Engineering, Materials Science, Applied Physics, Statistics, Data Science, Computer Science, or a related field.
• Strong expertise in statistical analysis, experimental design, optimization, machine learning, and predictive modeling.
• Experience developing analytical and machine learning models using Python or similar scientific computing environments.
• Understanding of AI, Generative AI, Large Language Models (LLMs), and AI-enabled engineering workflows, with the ability to combine data-driven insights with process and physical understanding.
• Excellent analytical, communication, and multi-functional collaboration skills to drive technical solutions and business outcomes.

Preferred Qualifications:

• Experience in semiconductor process development, manufacturing, process integration, equipment engineering, yield enhancement, or metrology.
• Hands-on experience with Virtual Process Modeling (VPM), Virtual Process Analysis (VPA), virtual metrology, Digital Twins, or other ML-based semiconductor modeling approaches.
• Proven ability to develop and deploy automated modeling, analytics, and data-processing pipelines in manufacturing environments.
• Familiarity with DRAM, HBM, advanced packaging technologies, and semiconductor process flows/travelers.
• Experience with advanced modeling platforms, workflow automation, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI solutions is highly desirable.

Job Profile(s):

Data Scientist 3 - Data Scientist 4

Relocation Level: TBD

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