Junji Hashimoto

Japan

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Summary

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Rockstar

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Top School

Junji Hashimoto is a Senior Infrastructure Engineer with 13 years of experience building high-performance systems, leading NoSQL strategy and large-scale migrations at GREE. He blends hardware-savvy ASIC and RTL design experience from a decade at Kawasaki Microelectronics with production functional programming—bringing Haskell into image distribution servers to improve reliability. An open-source contributor, he has optimized build and deployment for Hasktorch and fixed low-level filesystem and dlopen issues in the widely used Emscripten project, demonstrating deep systems and DevOps skills. Currently exploring verified computing, he applies Lean 4 to produce verified RTL (Sparkle) and formalize components across AI and hardware stacks. Colleagues rely on him for technical leadership that bridges operations, formal methods, and practical system delivery.

13 years of coding experience

19 years of employment as a software developer

University of Tokyo

Master's degree, Bioinformatics, Data mining and Visualization, Master's degree, Bioinformatics, Data mining and Visualization at Graduate School of Frontier Sciences. The University of Tokyo

Stackoverflow

Stats

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Github Skills (16)

c1710

cabal10

emscripten10

filesystem10

docker10

webassembly10

c1110

rust-wasm10

file-operations10

fileio10

dockers10

build-automation10

cicd10

ghc10

cuda9

Programming languages (21)

LeanC++BikeshedRustCMakefileVueGo

Github contributions (5)

hasktorch/hasktorch

Dec 2018 - Dec 2022

Tensors and neural networks in Haskell

Role in this project:

Back-end & DevOps Engineer

Contributions:73 reviews, 936 commits, 437 PRs in 4 years

Contributions summary:Junji's contributions primarily involved setting up and configuring the build and deployment process for the project. They added Dockerfiles for both CPU and GPU environments, including installing dependencies like GHC, Cabal, and necessary CUDA packages. They also integrated MKLML and set environment variables for compilation, indicating their focus on building and optimizing the project's infrastructure.

haskellneural-network

emscripten-core/emscripten

Aug 2016 - Dec 2016

Emscripten: An LLVM-to-WebAssembly Compiler

Role in this project:

Back-end Developer

Contributions:6 commits, 9 PRs, 20 comments in 4 months

Contributions summary:Junji primarily contributed to the Emscripten project by addressing issues related to system calls and file system operations. They fixed bugs in `dlopen` and `chdir` functions, specifically handling `RTLD_GLOBAL` and invalid paths. The user also introduced a new feature, `PROXYFS`, allowing the mounting of existing filesystems within the Emscripten environment, expanding its file system capabilities. These modifications demonstrate expertise in system-level programming and file system interaction within the context of WebAssembly.

compileremscriptenllvmwebassemblywasm

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