Zhengyang Qi is an AI research scientist in San Francisco with 11 years of experience building agentic coding systems and reinforcement-learning agents for complex, multi-party environments. He combines academic rigor—MS from Carnegie Mellon and multiple conference publications including ICLR and EMNLP—with product-driven engineering at Scale AI and Snorkel AI, where he designs LLM-as-a-judge frameworks and evaluation benchmarks. His background spans end-to-end ML systemization from low-latency image captioning and deduplication at Intuit to founding a startup that matched fashion inventory across dozens of retailers. Comfortable moving research into production, he blends multimodal model training, long-horizon RL, and practical deployment optimizations. A less obvious strength is his cross-disciplinary training in economics and mathematics, which informs principled evaluation and incentive-aware agent design.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.92/4.00, Bachelor of Science - BS, Computer Science, 3.92/4.00 at Georgia Institute of Technology
Bachelor of Arts - BA, Economics & Mathematics, 3.82/4.00, Bachelor of Arts - BA, Economics & Mathematics, 3.82/4.00 at Emory University
Master of Science in Intelligent Information Systems, Computer Science, 3.96/4.30, Master of Science in Intelligent Information Systems, Computer Science, 3.96/4.30 at Carnegie Mellon University School of Computer Science
Contributions:69 commits, 2 PRs, 90 pushes in 4 months
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