James Lamb
Senior Software Engineer - Infrastructure, Build, And Packaging (RAPIDS) at NVIDIA
Chicago, Illinois, United States
Summary
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Rockstar🎓
Top SchoolJames Lamb is a senior software engineer and data scientist with 11 years of experience building infrastructure, packaging, and MLOps tooling—currently driving RAPIDS packaging and build infrastructure at NVIDIA. He’s an active open-source maintainer (notably LightGBM) and frequent contributor across the PyData and RAPIDS ecosystems, with meaningful commits to projects like Dask, cuML, cuDF, and XGBoost. James blends deep packaging and CI/CD expertise (C++, R, Python packaging, Kubernetes, Terraform, Docker) with hands-on ML systems design, having built managed Dask/Jupyter platforms and production data pipelines at Saturn Cloud and SpotHero. He also teaches R programming at Marquette and co-organizes the Chicago MLOps community, reflecting a commitment to developer education and community building. An economist by training, he brings a quantitative, product-minded perspective to engineering problems and a penchant for finding portability issues early (author of pydistcheck).
11 years of coding experience
7 years of employment as a software developer
MS in Applied Economics (M.S.A.E.), Econometrics and Quantitative Economics, Marketing Research, MS in Applied Economics (M.S.A.E.), Econometrics and Quantitative Economics, Marketing Research at Marquette UniversityData Science Specialization, Data Science, Data Science Specialization, Data Science at Johns Hopkins University (via Coursera)Marian Catholic High SchoolMaster’s Degree, Data Science, Master’s Degree, Data Science at UC Berkeley School of InformationPython for Everybody Specialization, Computer Programming, Specific Applications, Python for Everybody Specialization, Computer Programming, Specific Applications at University of Michigan (via Coursera)
Stackoverflow
2,046reputation
116kreached
83answers
0questions
python
top-5%
Github Skills (56)
algorithm10
kubernetes10
xgboost10
c-language10
r-package10
python10
datahub10
r10
testing10
dataframes10
machine-learning10
cudf10
metadata10
machine-learning-algorithms10
cmake10
Programming languages (23)
Github contributions (5)
microsoft/LightGBM
Mar 2018 - Jan 2023
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:
ML Engineer
ML EngineerContributions:3229 reviews, 637 commits, 2065 PRs in 4 years 11 months
Contributions summary:James contributed to the R package for LightGBM by addressing issues related to CRAN, including documentation fixes, and by updating dependencies. They also implemented tests, and added features such as a framework for handling interaction constraints and support for improved prediction functions. The user demonstrated skills in R package development, statistical modeling and implementing core features of LightGBM.
kagglepythondata-mininglightgbmmicrosoft
dask/dask
Feb 2020 - Feb 2025
Parallel computing with task scheduling
Role in this project:Technical Writer
Technical WriterContributions:4 reviews, 11 PRs, 22 comments in 5 years
Contributions summary:James primarily focused on improving the project's documentation. They fixed typos, updated grammar and formatting in docstrings, and added examples and clarifications to the documentation. Additionally, the user addressed documentation issues, replacing outdated terms and correcting outdated links. Their contributions directly improved the clarity and usability of the project's documentation.
pythonschedulingparallelnumpydask
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