Irene Dea
Senior Software Engineer at MosaicML
Florida, United States
Summary
🤩
Rockstar🎓
Top SchoolIrene Dea is a Senior Software Engineer with a decade of experience building compiler internals, ML training infrastructure, and production systems at companies like Meta, MosaicML, and Databricks. She has deep backend and ML engineering chops—contributing to the Kotlin compiler’s call-resolution and type infrastructure and improving LLM training and logging tooling for large-scale model development. Her work spans low-level language design to data pipeline and DevOps fixes, reflecting a rare blend of compiler theory and practical ML productionization. Based in Florida and Berkeley‑educated in computer science, she brings a proven track record of shipping impactful features and refactors in both open-source projects and high‑velocity enterprise environments.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at University of California, Berkeley
Github Skills (21)
kotlin10
pytorch10
programming-language10
python10
testing10
pandas10
machine-learning10
internals10
ml10
llm10
type-system10
compiler-compiler10
deep-learning10
neural-network10
compiler10
Programming languages (4)
Github contributions (5)
mosaicml/llm-foundry
Aug 2023 - Apr 2025
LLM training code for Databricks foundation models
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:7 releases, 523 reviews, 227 PRs in 1 year 7 months
Contributions summary:Irene primarily focused on improving the LLM training code for Databricks foundation models, as evidenced by the nature of their commits. Contributions include fixing data loading errors, adding parameters to the regressions script for improved functionality, and addressing instantiation issues. Furthermore, the user updated dependencies, and added a script for MDS conversion of text files which improves data preparation. These changes enhance the project's data handling and testing capabilities.
deep-learningllmneural-networksnlppytorch
mosaicml/composer
Aug 2023 - Mar 2025
Supercharge Your Model Training
Role in this project:ML Engineer
ML EngineerContributions:2 releases, 94 reviews, 48 PRs in 1 year 7 months
Contributions summary:Irene's contributions center around extending the functionality of the Composer framework for machine learning. Their primary focus is on adding support for logging tabular data, specifically implementing a `log_table` method within multiple logger destinations. The code changes involve modifying existing logger classes to integrate Pandas DataFrames for structured data logging, improving logging capabilities for various use cases, including those related to Natural Language Processing. These updates demonstrate a focus on enhancing the framework's utility for ML training and analysis.
pytorchml-systemsdeep-learningneural-networksmachine-learning
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