Joseph Jin is a software engineer with a decade of experience building ML and inference systems, currently a Member of Technical Staff at Cogent Security in Berkeley. He holds a BS in EECS from UC Berkeley and has shipped production-focused work at Databricks (MosaicML and Serverless Real-Time Inference) and contributed to ML pipeline and training research during internships at Amazon and BAIR. Joseph blends research and engineering—co-authoring work on vision model training that reached ICML attention—with hands-on experience in performance, integration, DevOps, and embedded systems from startups to large companies. He’s also an entrepreneur, having co-founded a SkyDeck-backed safety startup, and brings a practical, security-minded approach to deploying scalable ML inference.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS Electrical Engineering and Computer Science, Bachelor of Science - BS Electrical Engineering and Computer Science at University of California, Berkeley
Contributions:4 commits, 1 push, 1 branch in 1 year 6 months
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