Applied AI for Yield and Diagnostics, Sr. Staff

Company:

Qualcomm Semiconductor Limited

Job Area:

Engineering Group, Engineering Group > ASICS Engineering

General Summary:

Location: Hsinchu, Taiwan (Onsite, 5 days/week)

Must have keywords: Agentic AI, Knowledge Graphs, GNNs, Computer Vision, Automation, Electronics/Semiconductor Industry

Nice to have keywords: ATPG/Memory Diagnostics, Yield, Analytics, Wafer Maps

General Summary:

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives communication and data processing transformation to help create a smarter, connected future for all. We are seeking a highly motivated and technically skilled AI Solutions Engineer to lead the development and deployment of artificial intelligence solutions aimed at improving silicon & assembly yield analysis and advanced diagnostics in semiconductor manufacturing, with particular emphasis on 3DIC and AI Accelerator chips. This role bridges the gap between advanced machine learning techniques and semiconductor diagnostics and yield engineering, with a focus on extracting actionable insights from complex data to enhance product quality and manufacturing efficiency.

Key Responsibilities:

• 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.

Minimum Qualifications:

• 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.

Preferred Qualifications:

• 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.

Preferred Skills:

• 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.

Why Join Us:

• Lead the AI-first transformation of a mission-critical yield and diagnostics organization.
• Build production AI solutions for flagship AI accelerator chips and advanced 2.5D and 3DIC programs at real scale.
• Shape the future of the chip industry by deploying AI that powers robotics, automotive, IoT, communications, and data center ecosystems worldwide.

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