Jakub Smid
AI RESEARCH ENGINEER at Rossum
Prague, Prague, Czechia
Summary
👤
Senior🎓
Top SchoolJakub Smid is an AI research engineer and leader with 11 years of experience, holding a Ph.D. in Artificial Intelligence from Charles University in Prague. He has grown from academic metalearning research into hands-on delivery, leading teams of 20+ ML engineers and driving end-to-end projects from PoC to production across cybersecurity, retail, manufacturing and public health. As ML Competency Lead and Tech Lead at Blindspot, he helped scale the company rapidly while owning presales, budgeting and strategic alignment of business and technical goals. He contributes to open-source ML tooling—having optimized algorithms and one-hot encoding performance in the widely used mlxtend library—underscoring a focus on practical, performant solutions. Currently at Rossum, he continues to blend research-grade rigor with product delivery, especially around AI strategy and governance. Colleagues describe him as a pragmatic innovator who turns academic insights into deployable systems that measurably reduce cost and risk.
11 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at Charles University in Prague
English, Czech, German
Stackoverflow
11reputation
379reached
0answers
1question
Github Skills (9)
pandas10
machine-learning10
python10
data-science10
scikit-learn9
scikit9
data-mining9
numpy8
microsoft-graph-api6
Programming languages (10)
Github contributions (5)
rasbt/mlxtend
Feb 2018 - Feb 2018
A library of extension and helper modules for Python's data analysis and machine learning libraries.
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 2 PRs, 11 comments in 8 days
Contributions summary:Jakub contributed to the `mlxtend` library, focusing on enhancing machine learning functionalities. Their work included optimizing existing algorithms such as the Apriori algorithm and improving the efficiency of one-hot encoding. The user also implemented a sparse representation option for one-hot encoding and updated documentation. These changes suggest an effort to improve the library's performance and usability for data science tasks.
supervised-learningpythondata-analysisdata-scienceunsupervised-learning
openml/openml-dotnet
Mar 2015 - Mar 2016
Contributions:58 commits, 78 pushes, 3 branches in 1 year
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