Benedikt Fuchs
Machine Learning Engineer at Research Industrial Systems Engineering (RISE) Forschungs-, Entwicklungs- und Großprojektberatung
Vienna, Austria
Summary
🤩
Rockstar🎓
Top SchoolBenedikt Fuchs is a Machine Learning Engineer based in Vienna with nine years of experience specializing in NLP and productionizing cutting-edge research. He has a strong competitive programming background (IOI, CERC/ICPC) and a track record of winning hackathons, which complements his pragmatic engineering work across cyberbullying detection, invoice processing, intelligent search, and automated chat analysis in multiple languages. As ML Engineer and tech lead at RISE, and previously as a fullstack data scientist at Cloudflight, he has defined best practices, reusable components, and training pipelines for cross-project quality and scalability. An active open-source contributor and maintainer, he contributed significant refactors and memory-optimizations to the widely used flair NLP framework and maintains a PyPI package for brown-clustering. He routinely bridges research and production, running an "NLP-Papers Bookclub" to disseminate ideas internally and keep teams aligned with the latest advances.
9 years of coding experience
5 years of employment as a software developer
Vienna University of TechnologyMatura, Computer Science, 1,25, Matura, Computer Science, 1,25 at htl-donaustadt
German, English
Stackoverflow
11reputation
1kreached
2answers
0questions
Github Skills (12)
pytorch10
word-embeddings10
machine-learning10
nlp10
python10
onnx9
sequence-labeling9
keras6
keras-layer6
tensorflow6
vectorization6
6
Programming languages (5)
Github contributions (5)
flairNLP/flair
Oct 2021 - Jan 2023
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Role in this project:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:2 releases, 73 reviews, 213 commits in 1 year 3 months
Contributions summary:Benedikt made several significant contributions related to the `flairnlp/flair` repository's word embeddings and machine-learning capabilities. They refactored existing code to remove dependencies on `gensim`, implemented quantization techniques for memory optimization, and refactored wordembeddings to function as a proper PyTorch layer, including the implementation of stable word embeddings. Further work included the addition of a MultiCoNer Dataset to support sequence labeling tasks.
nlppytorchnamed-entity-recognitionartsemantic-role-labeling
helpmefindaname/bot-skeleton
Dec 2019 - Jan 2024
Contributions:4 PRs, 2 pushes, 2 branches in 4 years 1 month
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