Carl Adorf
Senior Software Engineer, Machine Learning And Python RAPIDS at NVIDIA
Madison, Wisconsin, United States
Summary
🤩
Rockstar🎓
Top SchoolCarl Adorf is a Senior Software Engineer specializing in machine learning and Python within NVIDIA’s RAPIDS team, bringing 11 years of experience at the intersection of research and production-grade tooling. With a PhD from the University of Michigan, he has applied ML to materials inverse design and colloidal crystallization and now focuses on accelerating data-science workflows and ML libraries like cuML. As founder and long-term maintainer of the signac data and workflow framework and a core contributor to AiiDA and Materials Cloud, he blends scientific computing, DevOps, and open-science leadership to scale reproducible research. He routinely improves test coverage, CI workflows, and API ergonomics in high-profile open-source projects, and mentors distributed contributor teams across continents. Based in Madison, WI, he combines academic rigor with pragmatic engineering—often surfacing subtle UX and dependency fixes that materially improve developer productivity.
11 years of coding experience
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at University of MichiganVanderbilt UniversityBachelor of Science (B.Sc.), Bachelor of Science (B.Sc.) at Rheinisch-Westfälische Technische Hochschule Aachen
English, German
Stackoverflow
1reputation
0reached
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Github Skills (20)
algorithm10
dependency-management10
github-ci10
python10
aida10
machine-learning10
workflow-engine10
machine-learning-algorithms10
cicd10
numpy10
cuda10
githubaction-workflow10
cuml10
devops10
api-design9
Programming languages (17)
Github contributions (5)
aiidateam/aiida-core
Oct 2019 - Aug 2022
The official repository for the AiiDA code
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:183 reviews, 55 commits, 149 PRs in 2 years 10 months
Contributions summary:Carl's contributions primarily involved modifying the AiiDA core codebase to improve stability and maintainability. They implemented changes to suppress log messages during service status checks, refactored documentation, and revised the dependency management workflow. The user also updated testing and installation procedures to align with best practices, demonstrating knowledge of CI/CD pipelines and dependency management. Furthermore, they addressed dependency conflicts and improved Docker configuration.
aiidaworkflow-engineschedulerprovenancessh
rapidsai/cuml
Oct 2022 - Jan 2023
cuML - RAPIDS Machine Learning Library
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:359 reviews, 102 commits, 56 PRs in 3 months
Contributions summary:Carl primarily contributed to improving the codebase related to the cuML - RAPIDS Machine Learning Library. Their work includes fixing setup metadata, improving error messages within testing utilities, and implementing hypothesis-based tests for linear models, demonstrating a focus on enhancing code quality and test coverage. The user also refactored API decorators, indicating an effort to improve the codebase structure. They are heavily involved in adding new test modules and frameworks to increase the coverage of the project.
cudacumlnvidiadata-sciencegpu
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