Jonas Golde
PhD Student at Humboldt-Universität zu Berlin
Berlin, Germany
Summary
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Rockstar🎓
Top SchoolJonas Golde is a research-focused software engineer and PhD candidate in machine learning at Humboldt University Berlin with eight years of industry and academic experience. He combines consultancy-grade delivery from a five-year tenure at Deloitte with hands-on ML engineering demonstrated by applied scientist internships at Amazon. As a core contributor to the popular flairNLP framework, he enhanced sequence labeling by integrating multiple keyphrase extraction datasets and improving multilingual NER preprocessing, reflecting a practical focus on dataset and pipeline quality. Currently a research associate, he bridges applied research and production-ready tooling, with experience co-founding a startup that sharpened his product and engineering instincts. Based in Berlin, he brings a pragmatic blend of research rigor, open-source impact, and consulting discipline to ML systems engineering.
8 years of coding experience
3 years of employment as a software developer
Dr. rer. nat., Computer Science, Dr. rer. nat., Computer Science at Humboldt-Universität zu BerlinBachelor of Science, Management and Information Technology, Bachelor of Science, Management and Information Technology at DHBW Mannheim
Chinese, Spanish, French, English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
sequence-labeling10
pytorch10
machine-learning10
nlp10
python10
natural-language-processing10
datasets10
named-entity-recognition9
entity-extraction9
named-entity-extraction9
Programming languages (2)
Github contributions (5)
flairNLP/flair
May 2020 - Jan 2023
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:22 reviews, 158 commits, 54 PRs in 2 years 8 months
Contributions summary:Jonas primarily contributed to the development of new datasets and functionalities within the `flairnlp/flair` repository. Their work involved adding support for multiple keyphrase extraction datasets, including INSPEC, SEMEVAL2017, and SEMEVAL2010, indicating a focus on enhancing the sequence labeling capabilities of the framework. They also implemented a function to remove lines without annotations from the Finnish NER corpus. Additionally, they made code cleanup and documentation changes.
natural-language-processingpytorchnlpnamed-entity-recognitionsequence-labeling
whoisjones/flair
Dec 2020 - May 2021
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Contributions:1 review, 8 PRs, 65 pushes in 4 months
nlpartbertsimple-frameworklanguage-processing
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