Pieter Marsman
Machine Learning Engineer at Zero food waste
Zeist, Utrecht, Netherlands
Summary
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RockstarPieter Marsman is a Machine Learning Engineer based in Zeist, Utrecht, with 12 years of software engineering experience and a talent for bringing clarity and calm to complex codebases. Currently at Zero Food Waste, he applies ML to practical problems in sustainability, shipping reliable models and data pipelines in production. He is an active open-source contributor, notably improving functionality and test coverage for the widely used pdfminer.six PDF library by addressing encoding edge cases and preventing overflow errors. Pieter blends backend development and test automation expertise, ensuring robustness from implementation through quality assurance. Colleagues describe him as someone who seeks "peace and quiet" in code—meaning thoughtful, maintainable solutions rather than quick hacks.
12 years of coding experience
Github Skills (5)
unit-testing10
python10
pdf-parsing10
test-automation10
hex7
Programming languages (13)
Github contributions (5)
pdfminer/pdfminer.six
Jul 2019 - Nov 2022
Community maintained fork of pdfminer - we fathom PDF
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:12 releases, 137 reviews, 116 commits in 3 years 4 months
Contributions summary:Pieter primarily contributed to the functionality and testing of the `pdfminer.six` library. Their work involved adding new unit tests to the `test_encodingdb.py` file, aligning with the Adobe Glyph List Specification. They also addressed a reported overflow error by adding a corresponding test. Furthermore, the user modified the `name2unicode` function and enhanced the test suite with lowercase Adobe glyph name tests and key error handling.
extractionpythonpdfpdfminertext-extraction
pietermarsman/me-understanding-extremely-randomized-trees
Sep 2019 - Dec 2022
Me understanding Extremely Randomized Trees (Geurts, 2006)
Contributions:2 PRs, 3 pushes, 1 branch in 3 years 3 months
understandingpythonrandomizedmachine-learningextremely-randomized-trees
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