Jeremy Wohlwend
Co-Founder at Boltz
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolJeremy Wohlwend is a Cambridge-based machine learning engineer and founder with eight years of industry experience bridging research and production. After three years as a Staff Research Engineer at ASAPP, he co-founded Boltz to bring applied ML solutions to market, drawing on a deep academic foundation from MIT (BS, MEng, and ongoing PhD work in machine learning). He contributes to impactful open-source ML work—helping improve SRU recurrent architectures by implementing flexible computation paths and fixing CUDA kernel issues—which reflects a practitioner who moves ideas toward robust, efficient implementations. Jeremy combines hands-on CUDA and model engineering chops with product-minded startup leadership, making him equally comfortable debugging kernels or shaping technical strategy. An underappreciated strength is his track record of turning research prototypes into production-ready code, evidenced by contributions that improve training speed and reliability for RNNs.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Massachusetts Institute of Technology
Github Skills (6)
cuda10
pytorch10
deeplearning-ai10
recurrent-neural-networks10
deep-learning10
nlp9
Programming languages (2)
Github contributions (5)
asappresearch/sru
Aug 2019 - Oct 2019
Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 4 PRs, 6 pushes in 1 month
Contributions summary:Jeremy contributed to the `sru` repository, which focuses on training RNNs. Their commits primarily involved implementing features related to "custom_u" and "custom_v" in the SRUCell and SRU modules, allowing for more flexible computation. The user also addressed bug fixes related to the CUDA kernels and small typos in the code. The user's contributions centered on enhancing and debugging the SRU implementation.
nlppytorchgruarxivabs
asappresearch/flambe
Jul 2019 - May 2020
An ML framework to accelerate research and its path to production.
Contributions:13 releases, 439 commits, 186 PRs in 9 months
pythondata-sciencedeep-learningml-frameworkmachine-learning
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