Ian Taylor
Programmer at exe.dev
Berkeley, California, United States
Summary
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Rockstar🎓
Top SchoolIan Taylor is a seasoned programmer and compiler engineer based in Berkeley with nearly three decades of experience building and optimizing toolchains, compilers, and systems software. He spent the bulk of his career as a Principal Engineer on Google's Go team, and earlier held leadership roles in GNU toolchain work, startup CTO duties, and compiler hacking at companies like Wasabi Systems and C2 Microsystems. A longtime advocate for free and GNU software, his specialties span Go, C/C++, gcc/gdb/binutils, and build systems such as autoconf and automake, with hands-on experience in assemblers, linkers and debuggers. He also contributes to machine learning tooling—adding robust tests and improving proposal/target modules in the Luminoth computer vision toolkit—demonstrating a pragmatic blend of low-level systems expertise and ML engineering. Known for shipping reliable, maintainable infrastructure, he combines deep technical craftsmanship with a preference for open-source collaboration.
9 years of coding experience
35 years of employment as a software developer
Cambridge Rindge and LatinB.S., Computer Science, B.S., Computer Science at Yale University
English
Stackoverflow
524reputation
17kreached
12answers
0questions
Github Skills (18)
unit-testing10
python10
machine-learning10
deep-learning10
tensorflow10
object-detection10
computer-vision10
faster-rcnn10
test-automation10
coroutines6
cgo6
concurrency6
mips6
go6
client-go6
Programming languages (6)
Github contributions (5)
tryolabs/luminoth
Aug 2017 - May 2019
Deep Learning toolkit for Computer Vision.
Role in this project:
ML Engineer / Test Automation Engineer
ML Engineer / Test Automation EngineerContributions:79 commits, 36 PRs, 113 pushes in 1 year 9 months
Contributions summary:Ian primarily contributed to the testing of the RCNNTarget module within the Luminoth deep learning toolkit. Their work involved adding new tests, including those for the empty case and multiple overlap scenarios, to ensure the correct behavior of the RCNN target generation. The user also refactored and improved existing tests, and added a test for verifying the consistency of bounding box targets. In addition, the user wrote tests for the RCNNProposal module and fixed a test for multiple overlaps.
pytorchpythonvisiondeep-learningcomputer-vision
IanTayler/MinG
Apr 2017 - Apr 2017
Contributions:65 commits, 61 pushes, 10 branches in 14 days
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