David Bieber
Software Engineer at Google
Mountain View, California, United States
Summary
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Rockstar🎓
Top School
Top expert inPython Ecosystem Development and Data Science
David Bieber is a software engineer with 14 years of experience based in Mountain View, currently building software at Google after a Princeton BSE in Computer Science. He blends a research-minded approach to ML and systems with practical engineering, contributing to high-profile open-source projects like TensorFlow Datasets and Google Research (notably work on learned interpreter models and JAX refactors). His strengths include improving testing and documentation for production datasets, refining model architectures and training code, and simplifying developer tooling as seen in contributions to python-fire. Passionate about education, math, and puzzles, he brings an educator’s eye for clarity and pedagogy to code and docs, making complex systems easier to understand. Colleagues appreciate that he pairs deep technical curiosity with a focus on reliability and maintainability across ML and backend codebases.
14 years of coding experience
1 year of employment as a software developer
Princeton University
Stackoverflow
98reputation
2kreached
7answers
2questions
Github Skills (26)
python10
model-driven10
testing10
machine-learning10
datasets10
model-building10
tensorflow10
jax10
modeling10
documentation10
model-driven-development10
data-science8
refactor7
refactoring7
google-contacts-api6
Programming languages (15)
Github contributions (5)
google/python-fire
Feb 2017 - Dec 2022
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 releases, 27 reviews, 221 commits in 5 years 10 months
Contributions summary:David primarily contributed to the core functionality of the Python Fire library by updating existing code and adding new examples. They made changes to the code, improving documentation, fixing syntax, and correcting errors. The contributions include code changes that refined the library's core functionality and improved the user experience and examples.
command-line-interfacepythonpython-firecli
google-research/google-research
Oct 2018 - Jan 2023
Google Research
Role in this project:ML Engineer
ML EngineerContributions:15 commits, 1 PR, 1 push in 4 years 4 months
Contributions summary:David primarily contributed to the development of a learned interpreter model within the Google Research repository. Their work involved modifying and extending the existing codebase for IPAGNN, including adding new features like interpolants and adjusting model parameters. The commits demonstrate a focus on model architecture, training, and dataset integration, specifically relating to control flow programs. Furthermore, the user refactored code to utilize updated JAX functions.
googlemachine-learningai
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