Simon Kornblith is a seasoned research engineer with 20 years of experience at the intersection of machine learning, scientific computing, and software engineering, currently leading a pretraining team at Anthropic. He brings deep research chops from Google Brain and a neuroscience PhD from MIT, coupled with long-term product and engineering impact through roles like senior developer on Zotero. Simon is a prolific open-source contributor across high-profile projects—improving MNE-Python event detection, core Julia libraries (DataFrames, HDF5, StatsBase, GLM, PyCall), and Google Research demos—demonstrating expertise in numerical systems, statistics, and language interop. He blends rigorous experimental thinking with production-grade backend engineering, often focusing on correctness, performance, and compatibility across ecosystems. Based in Oakland, he combines academic rigor with practical engineering—something visible in both his neural-data research and low-level contributions to language runtimes and scientific toolchains.
19 years of coding experience
17 years of employment as a software developer
Ph.D., Brain and Cognitive Sciences, Ph.D., Brain and Cognitive Sciences at MIT
Contributions:557 commits, 5 comments in 6 years 2 months
Contributions summary:Simon appears to have been a back-end developer, primarily focusing on maintaining and expanding Zotero's translator codebase. The commits demonstrate a strong focus on debugging and improving existing translators. They are responsible for fixing issues, implementing new features, and adding support for various content providers within Zotero's ecosystem. The commits show the user making changes to add support for new content and services.
Save and load data in the HDF5 file format from Julia
Role in this project:
Back-end Developer
Contributions:133 commits, 19 PRs, 49 pushes in 4 years 4 months
Contributions summary:Simon primarily contributed to bug fixes and feature enhancements within the `hdf5.jl` repository. They addressed compatibility issues, improved type handling, and introduced features such as support for new data types and array indexing. Additionally, the user optimized the codebase through performance improvements, including changes to how data is read from the file and memory management.
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