Mathias Parisot
Research Engineer at Zeta Alpha
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolMathias Parisot is a Machine Learning Research Engineer with eight years of engineering experience, currently building retrieval-augmented QA and domain-adapted dense retrieval systems at Zeta Alpha. He holds an MSc in Artificial Intelligence from the University of Amsterdam and a strong practical background in Python, PyTorch, and information retrieval, with a published thesis on dense neural retrieval. His career blends research and product-minded engineering—co-founding a healthtech startup that diagnoses anaemia from smartphone data while shipping analytics and production-ready tooling at Zeta Alpha and previous internships including AWS and Optiver. Based in Amsterdam, he brings hands-on experience profiling distributed training on HPC clusters and deploying real-world ML systems, with a knack for turning academic ideas into pragmatic solutions.
8 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Computer Science, 9.2, Bachelor of Science - BS, Computer Science, 9.2 at Vrije Universiteit AmsterdamMaster of Science - MS, Artificial Intelligence, 8.36 (provisional), Master of Science - MS, Artificial Intelligence, 8.36 (provisional) at University of AmsterdamBachelor's degree, Engineering Science, Bachelor's degree, Engineering Science at Grenoble INP - Ense3
French, English, German
Github Skills (23)
complexity8
sentence-transformers7
convolutional-neural-networks7
heterogeneous6
nlp6
benchmark6
retrieval6
deep-learning5
datasets5
machine-learning5
pytorch5
squad5
inference4
information-retrieval4
bert4
Programming languages (3)
Github contributions (5)
matprst/sudoku
Jul 2020 - Oct 2023
Sudoku implemented in PyGame. Includes UI, solver, and online sudoku scraper
Contributions:1 review, 3 PRs, 11 pushes in 3 years 3 months
pythonpygamesolversudokupython3
matprst/PIA-on-CNN
Jun 2020 - Aug 2022
Official implementation of Property Inference Attacks on Convolutional Neural Networks:Influence and Implications of Target Model’s Complexity.
Contributions:1 review, 46 commits, 2 PRs in 2 years 3 months
complexityinfluencedeep-learninginferenceconvolutional
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