Summary
Fan Yang is a software engineer with nine years of experience building scalable systems, currently shaping content-safety and modeling infrastructure for Google Ads in the San Francisco Bay Area. He holds a Master’s in Computer Science from the University of Michigan and has hands-on experience across C/C++, Java, Python, Scala, Spark, databases, and modern ML fine-tuning techniques. At Google he applies prompt engineering, LoRA and few-shot strategies on Gemini models and leads agentic context optimization to improve classifiers. His background includes performance work on big-data query engines at Workday (35% scan speedups via vectorized operators) and backend contributions to Google's QUIC implementation, improving stability and rate-limiting. As a former Graduate Student Instructor he designs DBMS coursework and mentors students, blending research, teaching, and production engineering. He combines rigorous academic training with practical performance and ML deployment skills, often tackling low-level systems and high-level model tuning in the same codebase.
9 years of coding experience
2 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at University of Michigan
Bachelor’s Degree Electrical and Computer Engineering, Bachelor’s Degree Electrical and Computer Engineering at Shanghai Jiao Tong University
English, Chinese