Jan Krepl
Senior Machine Learning Engineer at Open Brain Institute
Geneva, Geneva, Switzerland
Summary
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Rockstar🎓
Top SchoolJan Krepl is a Senior Machine Learning Engineer based in Geneva with a decade of experience bridging quantitative finance and large-scale ML research. He has led ML teams and delivered production-ready systems at institutions like EPFL’s Blue Brain Project and Open Brain Institute, while also consulting through Toptal. Jan’s background in quantitative finance (ETH Zürich) and economics gives him a strong grounding in probabilistic modeling and risk-aware solutions, reflected in his open-source work on deepdow for deep-learning portfolio optimization. He focuses on building robust training infrastructure—early stopping, checkpointing, MLflow logging—and rigorous evaluation pipelines that move models from research to reproducible production. Colleagues describe him as a pragmatic problem-solver who combines academic rigor with hands-on engineering across neuroscience, finance, and industry projects.
10 years of coding experience
7 years of employment as a software developer
Master’s Degree Quantitative Finance, Master’s Degree Quantitative Finance at ETH ZürichBachelor's degree Economics, Bachelor's degree Economics at Charles University
Stackoverflow
4,060reputation
322kreached
118answers
0questions
tensorflow
top-5%
scikit-learn
top-1%
machine-learning
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nlp
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python
top-5%
Github Skills (24)
pytorch10
python10
evaluation10
machine-learning10
deeplearning-ai10
deep-learning10
scikit-learn10
metric10
mlflow9
convex-optimization9
natural-language-processing9
tensorflow9
tensorboard9
nlp9
pytest8
Programming languages (10)
Github contributions (5)
jankrepl/deepdow
Feb 2020 - Aug 2022
Portfolio optimization with deep learning.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 release, 7 reviews, 54 commits in 2 years 6 months
Contributions summary:Jan primarily contributed to the implementation of core machine learning components for portfolio optimization. They added benchmark models, including one-over-N, random, and singleton strategies, and implemented evaluation logic to assess model performance. Furthermore, they introduced the main framework, including callbacks for early stopping, model checkpointing, and MLFlow logging. These changes suggest a focus on building the core infrastructure for training, evaluating, and comparing deep learning models in the context of portfolio optimization.
deep-learningportfolio-optimizationfinancemachine-learningpytorch
openbraininstitute/neuroagent
Jan 2025 - May 2026
Contributions:358 reviews, 98 PRs, 612 pushes in 1 year 3 months
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