Job Description

Your responsibilities will include developing cutting-edge algorithms, creating robust product disposition solutions, and establishing a proactive deviation alert system for yield and quality, enhancing data infrastructure, mentor team members, and fostering innovation to maintain a competitive edge. Cumulative Yield Ownership: Be a key member of cross-functional teams, to lead impactful initiatives that significantly enhance overall yield and quality improvement, driving substantial benefits for the organization. Data Analysis for Yield Improvement and Reliability: Utilize in-house statistical tools, machine learning, and AI for engineering data analysis to enhance yields and reliability, integrating these improvements within the Dispo workflow. Models/Algorithm Development: Develop state-of-the-art algorithms, including Machine Learning and Deep Learning models, to advance data mining and pattern recognition for quality enhancement, yield improvement, wafer/die level screening and efficiency enhancement. Data Infrastructure Development: Collaborate with IT, Manufacturing, and Operations teams to develop and enhance the data infrastructure pipeline using cloud technologies to support reliability and engineering assessments. Cross-Functional Collaboration: Work closely with various cross-functional teams, including Fab, HBM Technology Development, HBM Design, System Development, and Quality/Reliability teams, to ensure the holistic development and successful shipping of end products. Promotion of Innovation: Promote innovation and drive changes that provide a technical advantage over competitors, maintaining the company's competitive edge in the market. AI/ML Advocate: Collaborate with cross-functional teams to develop, deploy, and validate AI/ML models aimed at enhancing key performance indicators (KPIs) such as Quality, Cost, Cycle Time, and Scale. Bachelor's degree in Computer Science with strong academic performance. Experience in the semiconductor industry is preferable. Experience building full-stack systems powered by GenAI, including RAG pipelines, vector databases, and LLM integration. Proven ability to orchestrate data engineering pipelines, including SQL query optimization, RESTful API development, and Docker containerization. Strong proficiency in machine learning, especially regression and classification models in Python. Experience with BI tools like Power BI and Tableau. Excellent communication skills to present findings and insights to non-technical stakeholders. Strong sense of responsibility and accountability towards assigned role with professional work ethic. Work on site in Singapore with international travel to Taiwan.

Skills Required