Dick Ameln
AI ML Engineer at Boskalis
Utrecht, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolDick Ameln is an AI/ML engineer with eight years of experience building production-ready deep learning systems, most recently at Boskalis following a multi-year research engineering role at Intel. He combines a strong academic foundation—cum laude MSc in Artificial Intelligence and interdisciplinary training in human movement sciences—with hands-on expertise in anomaly detection, metrics frameworks, and edge inference. On GitHub he contributed substantive refactors to the anomalib project, replacing custom evaluation code with TorchMetrics and reimplementing PatchCore in PyTorch to improve testability and edge deployment. Comfortable moving models from research to production, he has a track record of improving code quality, static typing, and maintainability in collaborative open-source and enterprise settings. A less obvious strength is his background in biomechanics and robotics, which gives him a practical edge in sensor-driven and time-series ML applications.
8 years of coding experience
5 years of employment as a software developer
PhD Course Advanced Musculoskeletal Modeling Techniques, Biomedical/Medical Engineering, PhD Course Advanced Musculoskeletal Modeling Techniques, Biomedical/Medical Engineering at Aalborg UniversityMaster of Science - MS, Artificial Intelligence, Cum Laude, Master of Science - MS, Artificial Intelligence, Cum Laude at University of Groningen
Github Skills (6)
computer-vision10
pytorch10
machine-learning10
unsupervised-learning10
anomaly-detection10
python10
Programming languages (1)
Github contributions (5)
open-edge-platform/anomalib
Dec 2021 - Jan 2023
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:10 releases, 710 reviews, 71 commits in 1 year 1 month
Contributions summary:Dick primarily focused on refactoring the project's evaluation metrics framework, transitioning from custom classes to the TorchMetrics library. They implemented changes to integrate TorchMetrics, updated existing tests, and addressed associated mypy and pylint issues. The user also rewrote the PatchCore anomaly map generator in PyTorch. Additionally, the user co-authored multiple commits, indicating collaboration.
hyper-parameter-optimizationstate-of-the-arthyperdetection-libraryexperiment-management
djdameln/anomalib
Feb 2023 - Mar 2025
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Contributions:7 reviews, 2 PRs, 265 pushes in 2 years 1 month
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