Summary
Ziqun Ye is a Research Engineer specializing in GenAI with eight years of experience applying machine learning and quantitative techniques across finance and enterprise software. Currently building a robotic financial advisor at Arta Finance, he previously led LLM fine-tuning, evaluation, and guardrail work at Oracle while contributing to an accelerated Data Science SDK. His background combines deep quantitative finance (MSc Computational Finance, Master of Mathematics in Statistics-Finance) with practical production ML engineering, from VaR and risk tooling to end-to-end ML pipelines. Ziqun is fluent in turning research-grade models into robust applications, particularly in financial contexts where data quality, model validation, and regulatory concerns matter. He blends academic rigor with product focus—an engineer who moves from Heston-model research and option pricing to shipping LLM-driven features. Based in Menlo Park, he brings a rare mix of quantitative finance pedigree and hands-on GenAI deployment experience.
8 years of coding experience
8 years of employment as a software developer
Master of Science (M.Sc.) Computational Finance, Master of Science (M.Sc.) Computational Finance at University of Washington
Machine Learning, Machine Learning at Coursera
Certificate NLP224n NLU224u CS229 CS110 CS234 CS231n, Certificate NLP224n NLU224u CS229 CS110 CS234 CS231n at Stanford University
Passed P FM MLC C MFE, Passed P FM MLC C MFE at Society of Actuary
Master of Mathematics Statistics-finance, Master of Mathematics Statistics-finance at University of Waterloo
Chinese, English