Cong Chen is a founding engineer with nine years of experience building AI-powered systems, currently leading development of personalized educational products in San Francisco. He blends research-grade machine learning (NLP, computer vision, reinforcement learning) with pragmatic engineering—shipping Langgraph-based agentic workflows, Streamlit UIs with OIDC, and MongoDB-backed personalization at Fastlearn.ai. Previously at Google he deployed low-latency TensorFlow pipelines that caught millions of Play Store spam requests daily while materially improving recall and throughput. His background includes high-impact scientific software at Lawrence Livermore where he trained U-Nets to 95% Dice scores and scaled signal compositing runtimes 18x, demonstrating both model and systems chops. Equally comfortable mentoring teams or working independently, he pairs a Stanford AI certificate and UC Berkeley CS/math degree with a knack for turning research prototypes into production-grade, cost- and time-saving solutions. An uncommon strength is his track record of aligning ML development tightly with business goals to cut content creation time and operational cost.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Arts (B.A.) Mathematics and Computer Science, Bachelor of Arts (B.A.) Mathematics and Computer Science at University of California, Berkeley
Computer Science, Computer Science at The Faculty of Engineering at Lund University
Graduate Certificate Artificial Intelligence, Graduate Certificate Artificial Intelligence at Stanford University
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