Mick Jermsurawong
Software Engineer at OpenAI
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolMick Jermsurawong is a software engineer with 11 years of experience building performant systems, currently at OpenAI after a six-year tenure at Stripe. He brings strong back-end and distributed-systems expertise—demonstrated by contributions to BIDMach where he implemented all-reduce workers and buffer optimizations for CPU/GPU-accelerated ML workloads. Mick holds a Master of Engineering in EECS from UC Berkeley and a BS in Computer Science from NYU Abu Dhabi, blending rigorous academic training with hands-on product experience across fintech and e‑commerce. He has a track record of improving communication and data-handling layers in production services, and tends to focus on efficiency gains that pay off at scale.
11 years of coding experience
8 years of employment as a software developer
Master of Engineering, Electrical Engineering and Computer Sciences, Master of Engineering, Electrical Engineering and Computer Sciences at University of California, BerkeleyBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at New York University Abu Dhabi
Github Skills (10)
machine-learning10
distributed-systems10
back-end-development10
python10
algorithms9
data-processing9
data-structure9
data-structures9
cprogramming-language7
c-language7
Programming languages (3)
Github contributions (5)
BIDData/BIDMach
Dec 2017 - May 2018
CPU and GPU-accelerated Machine Learning Library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:95 commits, 15 PRs, 109 pushes in 5 months
Contributions summary:Mick implemented core features for the `bidmach/bidmach` repository, focusing on adding and improving the all-reduce functionality. The commits involve the implementation of an all-reduce worker, including the addition of the allreducer and buffer optimizations. These changes likely involve core algorithms for distributed machine learning, as well as code for communication and data handling. The contributions suggest an emphasis on enhancing the efficiency and functionality of the machine learning library.
cudacpudeep-learninggpuaccelerated
vivekraghuram/IRL-captions
Oct 2017 - Nov 2017
Contributions:15 PRs, 42 pushes, 11 branches in 1 month
reinforcement-learninginverse-reinforcement-learningcaptionsreinforcementinverse
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