Marcus Chiam
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMarcus Chiam is an AI research engineer with a decade of experience building production-ready machine learning systems and open-source tooling, currently contributing to DeepMind’s Discovery team in London. Previously he was a core contributor to Google’s Flax library for JAX—fixing critical bugs, improving checkpointing, and shaping the next-generation NNX API that bridges functional performance with PyTorch-like ergonomics. His background spans quantitative data science in capital markets—where he shipped recommender systems, forecasting models and a provisional patent-worthy product—to bioinformatics research applying ML to drug response and interaction prediction. Marcus combines rigorous research instincts with practical engineering: he moves models from notebooks into robust libraries and dashboards used by analysts and researchers. An early career in lab work and music teaching hints at meticulousness and an ability to communicate complex ideas to diverse audiences.
10 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.Sc.) Bioinformatics and Computational Biology Specialist, and Molecular Genetics and Microbiology Major, Bachelor of Science (B.Sc.) Bioinformatics and Computational Biology Specialist, and Molecular Genetics and Microbiology Major at University of Toronto
Github Skills (6)
neural-network10
machine-learning10
jax10
python10
testing9
tensorflow8
Programming languages (5)
Github contributions (5)
google/flax
Oct 2022 - Jan 2023
Flax is a neural network library for JAX that is designed for flexibility.
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 219 reviews, 11 commits in 3 months
Contributions summary:Marcus contributed to the `google/flax` repository, which focuses on a neural network library for JAX. Their contributions included fixing dropout issues and merging code changes. They also updated documentation and examples, specifically in the context of getting started guides and Imagenet examples. Additionally, the user appears to have tested and validated checkpointing functionality.
jaxneural-network
chiamp/fast-reinforcement-learning
Nov 2021 - May 2022
Contributions:21 commits, 25 pushes, 1 branch in 7 months
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