Ming Min is a machine learning and full-stack engineer with 7 years of experience building scalable ranking, recommendation, and ads systems across top AI-driven companies in Palo Alto and the Bay Area. Currently a Member of Technical Staff at xAI after a senior ML engineering role at X, he has hands-on expertise in transformer modeling, model quantization, retrieval, and probabilistic identity matching from prior roles at TikTok. His academic background includes a PhD in Statistics and Applied Probability (financial mathematics) from UC Santa Barbara, reflecting strong quantitative rigor that he applies to production ML and multi-task learning pipelines. Ming combines research-grade modeling with production-savvy engineering—shipping large-scale systems that balance model accuracy, latency, and traffic strategy. An active coder who enjoys building web and mobile experiences, he brings both deep algorithmic insight and pragmatic product focus to complex ML problems.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Science, Business Administration, Bachelor of Science, Business Administration at Beijing University of Posts and Telecommunications
Master’s Degree, Financial Mathematics, GPA 3.9/4.0, Master’s Degree, Financial Mathematics, GPA 3.9/4.0 at Worcester Polytechnic Institute
Doctor of Philosophy - PhD, Statistics and Applied Probability, Financial Mathematics, 3.97/4.0, Doctor of Philosophy - PhD, Statistics and Applied Probability, Financial Mathematics, 3.97/4.0 at UC Santa Barbara
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