Stanley Shih
Software Engineer at Stanley's Test Company123
Sunnyvale, California, United States
Summary
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RockstarStanley Shih is a seasoned software engineer with 14 years of experience based in Sunnyvale, California, currently building software at Stanley's Test Company123. He brings strong full‑stack and ML-focused engineering skills, demonstrated by contributions to high-profile open-source projects such as TensorFlow.js, TensorBoard, and tfjs-examples where he improved documentation, example code, and uploader reliability. Stanley has a knack for improving developer experience and robustness—adding tests, fixing memory and overflow issues, and clarifying APIs and educational materials to help others adopt complex tooling. His work on the Common Lisp Koans highlights a less obvious strength: a dedication to pedagogy and language fundamentals that complements his ML and backend work. Comfortable across QA, backend, and ML example development, he blends hands-on coding with thoughtful documentation and test automation. Colleagues can expect a pragmatic engineer who values clarity, stability, and making hard systems approachable for users and learners.
14 years of coding experience
Github Skills (35)
unit-testing10
javascript10
python10
testing10
tensorflowjs10
machine-learning10
typescript10
tensorboard10
deep-learning10
trainings10
common-lisp10
typescript-types10
typescripts10
modeling10
grpc10
Programming languages (9)
Github contributions (5)
google/lisp-koans
May 2013 - Aug 2018
Common Lisp Koans is a language learning exercise in the same vein as the ruby koans, python koans and others. It is a port of the prior koans with some modifications to highlight lisp-specific features. Structured as ordered groups of broken unit tests, the project guides the learner progressively through many Common Lisp language features.
Role in this project:
Software Engineer (Lisp Koans Contributor)
Software Engineer (Lisp Koans Contributor)Contributions:69 commits, 28 PRs, 30 pushes in 5 years 3 months
Contributions summary:Stanley primarily contributed to improving the Common Lisp Koans project by addressing various issues and enhancing the learning experience. Their work included fixing broken koans, correcting typos in code and documentation, and refining error messages for better clarity. The user also made adjustments to the curriculum, such as reordering lessons, and incorporated contributions from other users. These changes collectively aimed to refine the Koans project and make it a better learning resource.
pythonlanguage-learningunitcommon-lispbroken
tensorflow/tfjs-examples
Apr 2018 - Oct 2019
Examples built with TensorFlow.js
Role in this project:ML Engineer
ML EngineerContributions:9 reviews, 27 commits, 43 PRs in 1 year 6 months
Contributions summary:Stanley primarily contributes to examples within the `tfjs-examples` repository, demonstrating a focus on TensorFlow.js and its applications. Their work includes creating a minimal example to get started with TFJS and improving the functionality, in addition to fixing vega-lite config to fix console warnings, and adding an example with custom layers, which showcases their understanding of TFJS APIs. The user also refactored the Boston Housing example to match the exposition in a draft book, which demonstrates a strong understanding of ML concepts and coding practices.
tensorflow-jsjavascripttensorflow
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