Henry Zhang
Product Manager at Cresta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolHenry Zhang is a product manager and former software engineer with 10 years of experience building AI-driven products and blockchain infrastructure, currently leading AI agent initiatives at Cresta in San Francisco. He blends hands-on ML engineering—contributing SQL algorithm support to the Softlearning RL framework—with strategic transformation experience from McKinsey, where he partnered with C-suite leaders. Earlier roles span developer relations for Celo validators, YouTube trust-and-safety ML, and NLP-driven recommendation research at Bloomberg, plus founding a high-growth edtech startup that scaled content and raised seed funding. Comfortable shifting between code, research, and go-to-market strategy, he focuses on creating large-scale social impact through technology and often operates at the intersection of product, ML, and operations.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Applied Science (BASc) Engineering Science, Bachelor of Applied Science (BASc) Engineering Science at University of TorontoTsinghua University High School
English, Chinese
Github Skills (8)
deep-reinforcement-learning10
machine-learning10
deep-learning10
python10
reinforcement-learning10
tensorflow9
sql9
deep-neural-networks8
Programming languages (6)
Github contributions (5)
rail-berkeley/softlearning
Dec 2018 - Jan 2019
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 2 PRs, 17 comments in 12 days
Contributions summary:Henry implemented and integrated the SQL algorithm, a reinforcement learning technique, into the Softlearning framework. This involved modifying existing code to support the new algorithm, including creating new algorithm classes and modifying experiment configurations. They also made changes to existing components to support the SQL algorithm. Furthermore, the user addressed issues related to deterministic actions and TensorFlow saveables within the SQL implementation.
entropydeep-reinforcement-learningtrainingframework-learningactor-critic-algorithm
bohan-zhang/geweek
Jun 2018 - Jan 2019
Contributions:66 pushes in 7 months
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