Kuba Weimann is a machine learning researcher and engineer with 10 years of experience building end-to-end deep learning systems, currently based in Berlin and working on large-scale ML at the Zuse Institute Berlin. He has led production-ready pipelines and optimized multi-node, multi-GPU training, shipping state-of-the-art self-supervised and federated ECG solutions trained on million-scale datasets. His work spans bioinformatics, time-evolving graph representation learning, and recommender systems, and includes pioneering studies on ECG transfer learning that improved downstream diagnostic accuracy. Equally comfortable in research and MLOps, he has productionized embedding and retrieval services on Triton and pgvector and prototypes that bridge clinical workflows to ML. Open to ML Research, Applied Scientist, or Senior ML Engineer roles across Berlin and EU-remote, he combines rigorous academic training (PhD/MSc/BSc) with pragmatic, privacy-aware system design.
10 years of coding experience
3 years of employment as a software developer
PhD, Bioinformatics, PhD, Bioinformatics at Freie Universität Berlin
Contributions:35 commits, 27 pushes, 1 branch in 3 months
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Kuba Weimann - Researcher - Machine Learning at Self-Employed