Jason Benn is a Founding Engineer and machine learning practitioner with 12 years of software experience and a track record of shipping practical ML systems from research to revenue. Based in San Francisco, he’s blended startup building (sole founding MLE at Sourceress that helped reach $2M ARR) with safety-focused research, coauthoring CVE-Bench and winning ICML spotlight and CAIS SafeBench prizes. He’s repeatedly built production-grade pipelines and automation—designing active learning to produce ~400 high-quality datasets, an “internal Kaggle” model competition system, and large-scale scraping and deployment workflows. More recently he contributed to model-diffing and post-training generalization research at Goodfire and now helps steer a collective-intelligence company as a founding engineer. Jason documents his learning in public and brings a rare mix of founder grit, ML research chops, and systems-level reliability engineering.
12 years of coding experience
10 years of employment as a software developer
Computer Science, Computer Science at Bradfield School of Computer Science
B.A., Economics, B.A., Economics at University of Virginia
Plug your electronic piano into your computer with a USB-to-MIDI cable, get live feedback as you play the best song ever written. No music theory required, includes several difficulty levels.
Contributions:101 commits, 65 pushes, 4 branches in 4 years 2 months
Contributions:49 commits, 4 PRs, 48 pushes in 3 years
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