Jacob Gao is an engineering manager in San Francisco with 11 years of experience applying machine learning and database techniques to ads targeting and candidate generation at scale. He holds a Ph.D. in Computer Science from Rice University, where his research explored solving large-scale ML problems using a relational database approach—an uncommon intersection that he continues to bring to production systems. At Pinterest he led teams building personalized and semantic retrieval for ads, translating research-grade models into high-throughput serving stacks. His early work spans systems and recommender problems at Facebook, Pandora, and Microsoft Research Asia, giving him deep experience in resource-aware distributed systems and temporal/graph-based recommendations. Known for bridging rigorous academic methods and pragmatic engineering, he often focuses on making complex model pipelines robust and efficient in real-world advertising environments.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Rice University
Bachelor of Engineering Computer Science, Bachelor of Engineering Computer Science at Zhejiang University
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