Rutger Fick
Head Of AI at CarCutter
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolRutger Fick is a Head of AI based in Paris with 10+ years of deep expertise in computer vision and machine learning, now focused on scaling generative AI into reliable, production-grade systems. He designs end-to-end ML platforms—from data pipelines and cost-aware GPU scaling to distributed inference—while embedding rigorous evaluation and robustness frameworks that translate research uncertainty into measurable product outcomes. A PhD-trained researcher and former lead on CE-marked medical AI products, he has authored 50+ publications and holds a patent, demonstrating a rare blend of clinical-grade rigor and product delivery. Rutger stays hands-on: he contributes to open-source medical imaging tooling (notably improving MAPMRI in the DIPY library) and continues to architect systems that balance scientific accuracy with operational constraints. He excels at leading senior teams and aligning AI innovation with business needs to ship scalable visual and generative solutions.
10 years of coding experience
5 years of employment as a software developer
Master of Science (MSc), Biomedical Engineering, Master of Science (MSc), Biomedical Engineering at Eindhoven University of TechnologyInternational Internship in Image Analysis and Registration, International Internship in Image Analysis and Registration at UC Santa Barbara
English, Dutch, French, German, Spanish
Github Skills (13)
mathematical10
diffusion-mri10
python10
modeling10
numpy10
data-structure9
algorithm9
data-structures9
algorithms9
statistics8
testing8
statistic8
machine-learning7
Programming languages (3)
Github contributions (5)
dipy/dipy
Oct 2015 - Oct 2018
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:154 commits, 3 PRs, 120 comments in 3 years
Contributions summary:Rutger made several enhancements and bug fixes related to the implementation of the MAPMRI model within the dipy library. They addressed issues in the code related to scale factor estimation, Laplacian regularization, and the positivity constraint. The user also added new q-space indices and improved existing functions, demonstrating a focus on the core functionality and mathematical accuracy of the diffusion MRI reconstruction methods.
signalpythonmicrostructurespatialtractography
AthenaEPI/dipy
Oct 2015 - Oct 2018
Diffusion MR Imaging in Python
Contributions:2 PRs, 96 pushes, 12 branches in 3 years
diffusionpythonimaging
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