Cody Wild
Research Software Engineer at Google DeepMind
Oakland, California, United States
Summary
🤩
Rockstar🎓
Top SchoolCody Wild is a Research Software Engineer with 11 years of experience building production-grade ML systems and research tooling, currently contributing to DeepMind on Jax-based modeling and prompt-tuning for large language model workflows. He combines a strong research pedigree—from co-authoring NeurIPS and ICLR work at CHAI to first-author preprints on layer-dependent sparsity—with hands-on systems work like a Flash-Attention variant and an autorater that cut human disagreement dramatically. Cody has driven modular, reproducible frameworks in both PyTorch and Jax, led engineering for NeurIPS BASALT benchmarks, and contributed testing and pipeline fixes to notable open-source projects such as Overcooked AI and MineRL. Based in Oakland, he’s equally at home automating robust test suites and probing representational learning questions, and he often bridges the gap between exploratory research and scalable experiment infrastructure.
11 years of coding experience
6 years of employment as a software developer
M.S. Analytics, M.S. Analytics at University of San FranciscoB.S. Mathematical Economics & Middle Eastern Studies, B.S. Mathematical Economics & Middle Eastern Studies at Tulane University
French, Arabic
Github Skills (14)
data-pipelines10
jestjs10
python10
data-pipeline10
testing10
machine-learning9
artificial-intelligence9
javascript8
data-loading8
git8
reinforcement-learning6
deep-learning5
deeplearning-ai5
pytorch4
Programming languages (6)
Github contributions (5)
minerllabs/minerl
Jun 2019 - Jul 2021
MineRL Competition for Sample Efficient Reinforcement Learning - Python Package
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 reviews, 35 commits, 13 PRs in 2 years
Contributions summary:Cody primarily focused on bug fixes and code improvements within the MineRL project. Their contributions involved correcting spelling errors, specifically in documentation and code comments. They also made changes to the data pipeline, including improvements to the buffered batch iterator. Additionally, they updated the download script to support BASALT vs Diamond downloads.
pythonminerlreinforcement-learningminerl-competitionlearning-python
HumanCompatibleAI/overcooked_ai
Jul 2019 - Jul 2019
A benchmark environment for fully cooperative human-AI performance.
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:8 commits in 3 days
Contributions summary:Cody's commits primarily focus on testing the Overcooked AI environment. They are seen modifying and expanding the test suite, specifically creating tests to validate the functionality of the game's logic and interactions, comparing results with expected outcomes. This involves adjustments to existing test cases, including modifying state and reward assertions. The commits demonstrate a focus on ensuring the correctness and reliability of the AI's behavior within the Overcooked environment.
pytorchhuman-aideep-learningreinforcement-learningmachine-learning
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