Jiayi Weng is a Research Engineer with nine years of experience building high-performance RL infrastructure and production ML systems, currently working on ChatGPT training and RLHF infra at OpenAI. He has a strong systems background demonstrated by authoring and optimizing EnvPool (C++/pybind11) to achieve over 1M FPS on DGX-A100 and improving multi-agent and data components in the popular Tianshou RL library. Comfortable across C++, Python, and Java/Android, he blends low-level optimization, test automation, and backend data engineering to deliver scalable, well-tested tooling. A Tsinghua undergrad and CMU computational data science master, he also holds a VizDoom competition win and a track record of shipping open-source projects that accelerate RL research.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering - BE, Computer Science and Technology, 3.77/4, Bachelor of Engineering - BE, Computer Science and Technology, 3.77/4 at Tsinghua University
Master's degree, Master of Computational Data Science (System Track), Master's degree, Master of Computational Data Science (System Track) at Carnegie Mellon University
Collection of undergraduate course homework and projects
Role in this project:
Full-stack Developer
Contributions:12 commits, 12 pushes, 1 branch in 6 months
Contributions summary:Jiayi primarily contributes to Java-based Android application development, focusing on a news application. Commits show changes in the MainActivity.java file, including UI elements, and RSS feed integration, and UI layout. The user also appears to be involved in the structure and design of the android application.
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Role in this project:
Back-end Developer & Test Automation Engineer
Contributions:21 releases, 136 reviews, 111 commits in 1 year 2 months
Contributions summary:Jiayi contributed to the testing of the `envpool` project, adding a new test suite for partial step functionality and benchmark tests. They implemented and tested a partial step functionality within the atari env and added tests for the implemented functionality in `envpool/atari/atari_envpool_test.py`. They also made adjustments to the `state_buffer_queue.h` and `async_envpool.h` files as part of the testing process.
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