Principal/Staff ML/AI Engineer — Product Engineering (STPG)

Job Title

Principal/Staff ML/AI Engineer — Product Engineering (STPG)

Role Summary

Lead development of applied machine learning and agentic AI solutions for semiconductor product engineering to improve yield, reduce test time, and automate engineering workflows. Work within the Product Engineering team on model development, deployment readiness, and integration with engineering systems.

Mentor and enable Citizen Data Scientists and collaborate with cross-functional teams (data engineering, inferencing architecture, manufacturing/engineering SMEs) to deliver production-ready ML solutions.

Experience Level

Senior level. Role targets Principal/Staff engineers. The posting indicates multi-year applied ML/AI experience (guidance: typically 5+ years for candidates with a Bachelor's/Master's or 3+ years for candidates with a PhD).

Responsibilities

Deliver ML/AI capabilities across the product engineering lifecycle and support production integration.

• Design, build, validate, and evaluate predictive models for yield, test time, reliability, and other manufacturing metrics using structured/tabular data.
• Develop agentic AI solutions to automate engineering tasks (test program validation, yield analysis, report generation) and integrate them with enterprise tools.
• Prepare and transform high-volume probe and wafer-fabrication inline data for ML applications in collaboration with cross-functional teams.
• Collaborate with inferencing architecture and deployment teams to ensure robust production integration and balanced inferencing.
• Identify opportunities to reduce test time, improve yield, and automate processes; contribute to technical papers, patents, and internal guidelines.
• Create learning paths, training modules, and provide ongoing mentorship to Citizen Data Scientists to raise ML competency across the organization.

Requirements

Core technical and professional skills required and desirable for the role. Educational degree details are summarized separately under Education Requirements.

• Must-have (technical): Strong proficiency in Python and data-processing libraries (Pandas, NumPy, scikit-learn); experience building and evaluating ML models for structured/tabular data (regression, tree-based models, gradient boosting).
• Must-have (practical): Experience applying ML techniques to production or engineering problems and working with large, complex datasets.
• Nice-to-have: Experience with deep-learning frameworks (TensorFlow, PyTorch) for unstructured data, exposure to agentic AI frameworks (e.g., LangChain), and familiarity with LLM integration into enterprise apps.
• MLOps and deployment: Knowledge of model lifecycle management, CI/CD for ML, production inference workflows, and inferencing at scale.
• Cloud and tooling: Awareness of distributed computing and cloud AI services (Azure ML, AWS SageMaker) and experience integrating with enterprise tools (JIRA, Confluence, SharePoint, Power Automate).
• Soft skills: Strong problem-solving, analytical thinking, and clear communication for cross-functional collaboration and mentoring.
• Preferred: Semiconductor or manufacturing analytics experience and hands-on experience building AI agents or workflow automation.

Education Requirements

Bachelor's or Master's degree in Computer Science, Electrical/Electronic Engineering, Data Science, or a related field with at least 5 years of machine learning/AI development and implementation experience; OR a PhD in a related field with at least 3 years of practical application experience (or a combination of research and industry experience).

About the Company

Company: Micron Technology

Headquarters: Boise, Idaho, USA

Micron Technology is a global leader in memory and storage solutions, dedicated to transforming how the world uses information. The company offers a diverse portfolio of high-performance DRAM, NAND, and NOR memory products under the Micron and Crucial brands. With a commitment to customer focus and technological innovation, Micron drives advancements in artificial intelligence, 5G, and other data-centric applications, empowering users to learn, communicate, and progress.