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Applied AI for Yield and Diagnostics, Sr. Staff
full-timeHsinchu

Summary

Location

Hsinchu

Type

full-time

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About this role


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.

Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.

If you would like more information about this role, please contact Qualcomm Careers.

Other facts

Tech stack
Agentic AI,Knowledge Graphs,GNNs,Computer Vision,Automation,Electronics,Semiconductor Industry,ATPG,Memory Diagnostics,Yield,Analytics,Wafer Maps,Python,SQL,MLOps,Deep Learning,Graph Databases

About Qualcomm

Delivering intelligent computing everywhere.

Team size: 10,001+ employees
LinkedIn: Visit
Industry: Semiconductor Manufacturing

What you'll do

  • The role involves owning the architecture of applied AI solutions for diagnostics and yield, and developing ML and computer vision models for semiconductor manufacturing. Additionally, it includes integrating AI solutions into production workflows and mentoring junior engineers.

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Frequently Asked Questions

What does a Applied AI for Yield and Diagnostics, Sr. Staff do at Qualcomm?

As a Applied AI for Yield and Diagnostics, Sr. Staff at Qualcomm, you will: the role involves owning the architecture of applied AI solutions for diagnostics and yield, and developing ML and computer vision models for semiconductor manufacturing. Additionally, it includes integrating AI solutions into production workflows and mentoring junior engineers..

Why join Qualcomm as a Applied AI for Yield and Diagnostics, Sr. Staff?

Qualcomm is a leading Semiconductor Manufacturing company.

Is the Applied AI for Yield and Diagnostics, Sr. Staff position at Qualcomm remote?

The Applied AI for Yield and Diagnostics, Sr. Staff position at Qualcomm is based in Hsinchu, Taiwan. Contact the company through Clera for specific work arrangement details.

How do I apply for the Applied AI for Yield and Diagnostics, Sr. Staff position at Qualcomm?

You can apply for the Applied AI for Yield and Diagnostics, Sr. Staff position at Qualcomm directly through Clera. Click the "Apply Now" button above to start your application. Clera's AI-powered platform will help match your profile with this opportunity and guide you through the application process. You can also learn more about Qualcomm on their website.