Charles Weill
Member Of Technical Staff at xAI
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolCharles Weill is a machine learning engineer and founder with 11 years of experience building production ML systems and developer tools. After a research-engineering stint at Google where he helped develop and ship the TensorFlow AdaNet AutoML library, he founded CreatorML (YC W23) to apply ML to YouTube creator growth. He’s an active open-source contributor across ML and tooling—work on tensorflow/adanet and the popular gotests generator shows comfort with both deep learning internals and practical developer ergonomics. Now based in New York and joining xAI’s Voice team, he blends research rigor with product-focused engineering. Charles holds M.Eng. and B.S. degrees from Cornell and brings a knack for refactoring complex codebases to be modular and testable—an often-hidden driver of long-term product velocity.
11 years of coding experience
11 years of employment as a software developer
YC W23 Founder, YC W23 Founder at Y CombinatorMaster of Engineering (M.Eng.) Computer Science, Master of Engineering (M.Eng.) Computer Science at Cornell University
English, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (18)
python10
machine-learning10
go-testing10
keras10
go10
tensorflow10
automl10
golang10
testing9
mstest9
teste9
code-generation9
deep-learning9
testing-library9
ui-testing9
Programming languages (7)
Github contributions (5)
tensorflow/adanet
Jun 2018 - Jul 2020
Fast and flexible AutoML with learning guarantees.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:11 releases, 247 commits, 74 PRs in 2 years 1 month
Contributions summary:Charles primarily worked on removing dependencies and refactoring the codebase to improve the project's modularity. They also refactored and created several helper functions, test classes and example codes to integrate with the Google internal build system, PiperOrigin. The user modified the code to include a variety of Keras layers, hinting at a focus on modifying and building the core machine learning aspects of the codebase. The code contributions show a strong understanding of the TensorFlow framework, AdaNet, and the related ML domain.
automlmachine-learningflexibledeep-learningdistributed-training
cweill/gotests
Jan 2016 - May 2021
Automatically generate Go test boilerplate from your source code.
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:11 releases, 19 reviews, 189 commits in 5 years 5 months
Contributions summary:Charles primarily contributed to the development and maintenance of the `gotests` project, a Go code generator for test boilerplate. Their work focused on refactoring the codebase, renaming packages, and fixing bugs related to test generation. The user demonstrated a solid understanding of Go programming, testing methodologies, and the project's core functionality, which involved parsing and generating test code. Their contributions improved the tool's robustness and usability.
gogolang-testingtable-driven-testtesting-toolscommandline
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