Applied AI for Yield and Diagnostics, Sr. Staff

Own the architecture of applied AI solutions for diagnostics and yield, from GenAI assistants that help engineers, to autonomous agents that plan, call tools, and execute safe next actions. Develop and deploy ML and computer vision models that interpret wafer maps, memory bitmaps, defect and FA images, and layout patterns, fusing signals with logs to speed triage and repair decisions. Model domain knowledge as graphs and apply GNNs to reveal relationships, cluster suspects, and suggest likely root causes. Integrate AI solutions into high-volume production diagnostics and analytic workflows so insights are directly actionable in existing tools. Establish reliable data and retrieval foundations with pipelines, versioning, and grounded retrieval over reports and runbooks, with clear lineage and quality checks. Drive adoption across package assembly, silicon yield, diagnostics, and FA by converting manual analyses into durable automation. Provide guidance and mentorship to junior engineers as we evolve to an AI-first team. Bachelor's degree in Science, Engineering, or related field and 6+ years of ASIC design, verification, validation, integration, or related work experience. OR Master's degree in Science, Engineering, or related field and 5+ years of ASIC design, verification, validation, integration, or related work experience. OR PhD in Science, Engineering, or related field and 4+ years of ASIC design, verification, validation, integration, or related work experience. Master's or PhD in CS, ML/AI, or related field, or equivalent practical experience. 7+ years building ML/AI systems, including at least 3 years in the field of electronics or semiconductor manufacturing. Strong Python and SQL, plus experience with distributed data processing, feature engineering, and model serving at scale. Hands-on experience with agent frameworks and orchestration of multi-tool chains for automated decision making. Track record shipping automation that replaced routine analysis or materially reduced analysis time. Experience with at least two of the following: deep learning, knowledge graphs or GNNs, computer vision, multimodal models, retrieval and grounding, fine-tuning. Understanding of 3DIC, STCO, and the interplay among design, process, and package for yield. Diagnostics and yield management systems; familiarity with wafer maps, scan diagnosis, memory bitmaps. Graph databases, vector stores, and hybrid retrieval across text, tables, and images. LLM tool orchestration, function calling, and RAG in production. MLOps, observability, evaluation design, security, and data governance.