Michael Chang
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Chang is a research-driven software engineer and Ph.D. student at UC Berkeley with a decade of experience building production-grade AI systems, currently a Member of Technical Staff at OpenAI working on Sora. Previously at DeepMind he led novel long-memory, low-latency capabilities for Project Astra and co-authored empirical evaluations behind Gemini 1.5 Pro’s hours-long video understanding, contributing to several key figures and tables. His background spans applied research internships and engineering roles at Google, Meta, and MIT, and he translates research into robust implementations—evidenced by RL-focused commits to Berkeley’s deep RL course repos. Based in San Francisco, he combines deep academic training with hands-on system and model engineering for real-time multimodal assistants.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of California, BerkeleyBachelor of Science (B.S.) Electrical Engineering and Computer Science, Bachelor of Science (B.S.) Electrical Engineering and Computer Science at Massachusetts Institute of TechnologyIndependent CourseworkHigh School Diploma Valedictorian, High School Diploma Valedictorian at Northwood High School
Github Skills (7)
gymnasium10
openai-gym10
machine-learning10
python10
reinforcement-learning10
tensorflow9
deep-reinforcement-learning9
Programming languages (8)
Github contributions (5)
berkeleydeeprlcourse/homework
Aug 2018 - Nov 2018
Assignments for CS294-112.
Role in this project:
ML Engineer
ML EngineerContributions:34 commits, 27 pushes in 2 months
Contributions summary:Michael primarily worked on modifying the `lunar_lander.py` file, which is part of a reinforcement learning assignment. The commits indicate changes to the action space discretization, observation space, and reward structure of the LunarLander environment. The user also fixed a type error and made updates to the `train_pg_f18.py` file, which likely involves training a policy gradient agent. The user also appears to be working on exploration strategies within the same environment.
mbchang/langchain
Mar 2023 - Jun 2023
⚡ Building applications with LLMs through composability ⚡
Contributions:109 pushes, 40 branches in 2 months
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