Eddie Bergman
Senior Machine Learning Engineer at DBtune
Malmo, Skåne County, Sweden
Summary
🤩
Rockstar🎓
Top SchoolEddie Bergman is a Senior Machine Learning Engineer with a decade of experience building and scaling AutoML and hyperparameter optimization systems, currently working at DBtune in Malmö. He blends academic rigor from his PhD and research engineer role at University of Freiburg with hands-on engineering—maintaining widely used open-source tooling (~60k monthly downloads) and contributing to prominent projects like auto-sklearn and TabPFN. Eddie has deep expertise in multi-fidelity optimization, fairness-aware AutoML and per-instance algorithm selection, and has taught master courses on these topics. His contributions span core backend features, evaluation leaderboards, visualization tooling, and documentation, showing a rare mix of research, production coding, and communication. Colleagues appreciate that he moves between paper ideas and production fixes fluently—adding practical metrics and robustness to foundation models for tabular data while keeping reproducibility and usability front of mind.
10 years of coding experience
5 years of employment as a software developer
The Highschool RathgarBachelor's degree, Computer Science, First Class Honours, Bachelor's degree, Computer Science, First Class Honours at Trinity College Dublin
Stackoverflow
306reputation
53kreached
6answers
0questions
Github Skills (28)
restructuredtext10
python10
sphinx10
data-science10
matplotlib10
scikit10
pandas10
machine-learning10
rs10
datatable10
automl10
tabular10
scikit-learn10
automated-machine-learning10
documentation10
Programming languages (10)
Github contributions (5)
automl/auto-sklearn
Jul 2021 - Nov 2022
Automated Machine Learning with scikit-learn
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:5 releases, 358 reviews, 336 commits in 1 year 4 months
Contributions summary:Eddie implemented and refined the `leaderboard` functionality, crucial for evaluating and comparing models within the automated machine learning framework. This included the creation of core functionality, bug fixes, and enhancements involving sorting, ranking, and filtering models, with a focus on classification tasks. Furthermore, they modified core Python files relating to the estimators themselves. The user also contributed to unit tests and documentation improvements.
pythonmeta-learningmetalearningbayesian-optimizationhyperparameter-tuning
automl/SMAC3
Jul 2021 - Aug 2022
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Role in this project:Technical Writer
Technical WriterContributions:48 reviews, 88 commits, 28 PRs in 1 year 1 month
Contributions summary:Eddie's commits primarily focus on updating and refining the documentation for the SMAC3 project. They have fixed broken links, corrected table of contents labels, and improved the formatting of quickstart examples. The changes include general improvements to the documentation layout, ensuring clarity and accuracy in the provided guides and examples. Their work has streamlined the user experience with improved navigation and corrected references.
bayesian-optimizationhyperparameter-tuninghyperparameterautomated-machine-learninggaussian-process
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