Jose Javier
Research Scientist at DataBricks mosaicml
San Francisco, California, United States
Summary
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RockstarJose Javier is a research scientist focused on large language model pretraining and fine-tuning, bringing an MIT PhD and hands-on ML engineering experience at MosaicML and Databricks. In two years of industry work he has shipped optimizations in LLM training—improving in-context learning, compiling GLU layers, and integrating Triton RMSNorm—to boost performance in the widely used mosaicml/llm-foundry codebase. Based in San Francisco, he blends rigorous academic training with pragmatic engineering, tackling data loading, configuration, and logging issues that make research code production-ready. Co-author of The Missing Semester, he contributes to open-source tooling and emphasizes practical, reproducible ML workflows.
2 years of coding experience
Github Skills (9)
neural-network10
pytorch10
triton10
deep-learning10
python10
llm10
data-loading9
configuration-management9
nlp8
Programming languages (2)
Github contributions (5)
mosaicml/llm-foundry
Oct 2023 - Aug 2024
LLM training code for Databricks foundation models
Role in this project:
ML Engineer
ML EngineerContributions:21 reviews, 22 PRs, 41 pushes in 10 months
Contributions summary:Jose contributed to the LLM-Foundry project by implementing and refining functionalities related to in-context learning and model optimization. Key contributions include modifying the `CodeEval` process, compiling the GLU layer for performance improvements, and integrating Triton RMSNorm for optimized normalization. The user also addressed issues related to configuration, data loading, and logging within the project.
deep-learningllmneural-networksnlppytorch
josejg/llm-foundry
Oct 2023 - Aug 2024
LLM training code for MosaicML foundation models
Contributions:86 pushes, 25 branches in 10 months
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