Nicolas Känzig

Staff Machine Learning Engineer at kaiko.ai

Zurich, Zurich, Switzerland
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Summary

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Senior
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Top School
Nicolas Känzig is a Staff Machine Learning Engineer based in Zurich with 8 years of cross-industry experience building ML-driven products from lab prototypes to production. He currently leads development of Kaiko.ai’s Data & AI platform that equips hospital physicians with advanced AI tools for cancer research, blending clinical impact with scalable engineering. His background spans healthtech, e-commerce, venture capital, electronics, aviation and education, giving him strong domain adaptability and a knack for translating research into practical systems. Trained at ETH Zürich in electrical engineering, he pairs hardware-aware thinking (FPGA and embedded experience) with applied ML, a combination that helps him optimize end-to-end data and model pipelines. Previously he supported early-stage startups at AI Fund and built customer-facing ML platforms at Rappi, demonstrating both product-focus and prototyping velocity.
code8 years of coding experience
job7 years of employment as a software developer
bookMaster of Science - MS Electrical Engineering and Information Technology, Master of Science - MS Electrical Engineering and Information Technology at ETH Zürich
languagesGerman, English, Spanish, French
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Github Skills (86)

foundation-models10
evaluation-framework10
image-segmentation10
machine-learning10
segmentation10
scalable9
object-detection9
image-processing9
python9
deep-learning9
image-classification9
pytorch9
imaging9
data-science9
image-augmentation9

Programming languages (6)

TypeScriptC#JavaCJupyter NotebookPython

Github contributions (5)

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nkaenzig/ml-workshop

Aug 2019 - Oct 2019

Slides & Notebooks for the Machine Learning Workshop
Contributions:25 commits, 20 pushes in 2 months
pythondata-scienceworkshopmachine-learningnotebooks
kaiko-ai/eva

Mar 2024 - Apr 2025

Evaluation framework for oncology foundation models (FMs)
Contributions:1 release, 328 reviews, 200 PRs in 1 year
evaluation-frameworkfoundation-modelsmachine-learningoncology
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