Tanja Bunk
Tech Lead at Rasa
Berlin, Germany
Summary
🤩
Rockstar🎓
Top School
Top expert inNatural Language Processing and Machine Learning Technologies
Tanja Bunk is a Berlin-based tech lead with 12 years of experience building scalable backend systems and production ML tooling, currently leading engineering at Rasa. She combines hands-on machine learning engineering with backend architecture, having shipped NLP tooling at Zalando (co-building the open-source flair library) and contributed to prominent open-source projects like Rasa and Flair. Her work emphasizes code quality and maintainability—evident from test refactors and modularization efforts—and she has practical experience integrating datasets and trainers for text classification. Comfortable across microservices, messaging infrastructure, and ML model pipelines, she excels at turning research-grade models into reliable, production-ready services. A detail-driven engineer, she often focuses on developer experience and test structure improvements that quietly raise the bar for team productivity.
12 years of coding experience
7 years of employment as a software developer
Master of Science (M.Sc.) IT-Systems Engineering, Master of Science (M.Sc.) IT-Systems Engineering at Hasso-Plattner-InstituteBachelor of Science (B.Sc.) IT-Systems Engineering, Bachelor of Science (B.Sc.) IT-Systems Engineering at Hasso Plattner Institute
Github Skills (19)
pytorch10
conversation10
python10
testing10
machine-learning10
text-classification10
forms10
sdk10
rasa10
nlp10
architecture9
pytest9
apidoc9
data-processing9
api9
Programming languages (5)
Github contributions (5)
RasaHQ/rasa-sdk
Apr 2019 - Sep 2020
SDK for the development of custom actions for Rasa
Role in this project:
Backend Developer
Backend DeveloperContributions:2 reviews, 127 commits, 30 PRs in 1 year 6 months
Contributions summary:Tanja's commits primarily focused on code reformatting and simplification, particularly within the tests for the `rasa-sdk`. They made changes to improve the clarity and readability of the test code. The user's contributions included reformatting code using 'black' and removing redundant code, demonstrating a focus on code quality and maintainability within the testing framework of the SDK. These changes impacted the quality of the testing structure.
custom-actionspythonrasasdk
flairNLP/flair
Jul 2018 - Apr 2019
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:326 commits, 113 PRs, 275 pushes in 8 months
Contributions summary:Tanja's contributions focused on refactoring and modularizing the codebase. They moved existing machine learning models into a dedicated model folder and added documentation to various components. The user also created a trainer package for machine learning tasks and implemented data fetchers for the IMDB and AG_NEWS datasets for text classification, indicating a focus on integrating these datasets into the framework.
nlppytorchnamed-entity-recognitionartsemantic-role-labeling
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.