Jun Gong
Principal Machine Learning Engineer at Biohub
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJun Gong is a Principal Machine Learning Engineer based in Mountain View with 11 years of experience building and leading ML and infrastructure teams for large-scale, production ML systems. He has led ML engineering at Biohub and driven foundational biology model training and serving at EvolutionaryScale, while previously shaping Ray AIR and RLlib as a Senior Staff engineer and committer at Anyscale. His background includes published work applying deep reinforcement learning to real-world robotics and flight control (Nature) and large fleet control at Google/Project Loon, reflecting a blend of research rigor and production-grade engineering. Jun is an active open-source contributor to Ray, where his RLlib fixes and tutorial work helped make scalable reinforcement learning and recommendation-system tutorials more robust and accessible. He is skilled at bridging research and production—building wrappers and environments to adapt research simulators (RecSim) into RLlib pipelines—and mentors both ML and data engineering teams. With a PhD in Computer Science and a track record across Google, Facebook, and cutting-edge startups, he combines systems-level thinking with hands-on ML engineering.
11 years of coding experience
18 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Fudan UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Northeastern University
Github Skills (13)
gymnasium10
openai-gym10
pytorch10
machine-learning10
rllib10
python10
reinforcement-learning10
tensorflow9
recommendation-system9
notebook8
data-science8
documentation7
deep-learning7
Programming languages (7)
Github contributions (5)
ray-project/ray
Aug 2021 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:1746 reviews, 183 commits, 538 PRs in 1 year 5 months
Contributions summary:Jun primarily contributed to the RLlib library, specifically within the reinforcement learning frameworks. Their contributions centered around fixing configuration issues and documentation errors within the RLlib library's example files and concepts. They fixed a seed setting issue in the single vectorized sub-environments and enhanced learning rate and entropy coeff scheduling.
pythonconsistsruntimetensorflowserving
anyscale/academy
Oct 2022 - Nov 2022
Ray tutorials from Anyscale
Role in this project:ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:10 commits, 10 pushes, 2 branches in 6 days
Contributions summary:Jun contributed to the development of a reinforcement learning environment based on RecSim for a recommendation system tutorial. The code modifications involved creating wrappers to adapt the RecSim environment for RLlib, including fixing observation spaces, converting action spaces, and scaling rewards. Additionally, the user integrated this environment within a tutorial notebook showcasing offline bandit and offline RL techniques for recommendation systems.
raypythonmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.