Principal/Staff ML/AI Engineer, Product Engineering, STPG

Machine Learning Development: Build predictive models for yield, test time, and reliability analysis using structured/tabular data Use advanced machine learning techniques to analyze complex datasets and generate actionable insights that improve manufacturing and operational performance Build and validate ML workflows for large-scale semiconductor datasets Develop intelligent agents to automate engineering tasks (e.g., test program validation, yield analysis, report generation) Work with cross-functional teams to access, clean, and structure high-volume probe and wafer fabrication inline data to generate insightful information for ML/AI applications Collaborate with the inferencing architecture team during deployment phases to ensure successful and balanced production integration Contribute to technical papers, patents, and internal guidelines Guide and upskill Citizen Data Scientists (CDS) in applying advanced ML techniques, guidelines, and analytics tools Build structured learning paths and hands-on training modules to strengthen CDS capabilities in machine learning and data-driven problem solving Bachelor's or Master's degree in Computer Science, Electrical/Electronic Engineering, Data Science, or a related field with at least 5 years of experience in machine learning/AI development and implementation PhD in a related field, accompanied by at least 3 years of practical experience in application (or a combination of research and industry involvement) 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 Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) for processing unstructured data (images, text, logs) and scalable model creation and deployment Exposure to agentic AI concepts and frameworks (e.g., LangChain, Microsoft Copilot Studio) for building autonomous workflows and orchestration Understanding of LLM-based solutions and their integration into enterprise applications Knowledge of MLOps practices, including model lifecycle management, CI/CD for ML, and production inference workflows for robust deployment Awareness of distributed computing and cloud-based AI services (e.g., Azure ML, AWS SageMaker) for scalable inferencing. Strong problem-solving and analytical thinking Excellent communication and ability to work in cross-functional teams with semiconductor subject-matter experts for technical exchange and management updates Semiconductor industry or manufacturing analytics background Hands-on experience with AI agents or workflow automation Knowledge of enterprise integration tools (Power Automate, SharePoint connectors)