Edward Schmuhl
Machine Learning Engineer at Weaviate
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolEdward Schmuhl is a Machine Learning Engineer based in Berlin with 7 years of experience building AI products and developer experiences that drive adoption. He contributes to industrial-strength NLP tooling—most notably adding serialization, checkpointing, and custom token/span attributes to the widely used spaCy library—while shipping production-ready pipelines and profiling CPU/GPU performance. At companies like Explosion, Weaviate and Jina AI he has implemented end-to-end ML workflows from data collection and annotation to model training and deployment, and helped deliver an AI-powered IT service chatbot and domain-specific pipelines such as Healthsea. An active open-source creator and educator, he produces example projects, blog posts, videos and animated visualizations to make complex ML topics accessible. His indie game development background informs a pragmatic, design-minded approach to tooling and UX for developers. Comfortable across Python, PyTorch, Thinc and cloud platforms, he blends research-driven models with practical engineering for real-world NLP applications.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Applied Science - BASc, Medieninformatik, 1.6, Bachelor of Applied Science - BASc, Medieninformatik, 1.6 at Berliner Hochschule für Technik (BHT)
Github Skills (13)
json10
nlp10
spacy10
python10
natural-language-processing10
cython9
machine-learning9
artificial-intelligence8
data-science8
ai8
deeplearning-ai7
deep-learning7
testing6
Programming languages (8)
Github contributions (5)
explosion/spaCy
Jul 2021 - Jan 2023
💫 Industrial-strength Natural Language Processing (NLP) in Python
Role in this project:
Backend Developer
Backend DeveloperContributions:68 reviews, 24 commits, 34 PRs in 1 year 6 months
Contributions summary:Edward contributed to the spaCy library by implementing new features, fixing bugs, and improving the JSON serialization capabilities. They added a new parameter for saving model checkpoints during pretraining and addressed an assertion error in the static vectors module. The user also added the ability to include custom attributes for tokens and spans in the JSON conversion, enhancing data handling. Furthermore, they added a new console logger for training which improves the development experience.
fairness-mlpythondata-preprocessinglanguage-processingtokenization
weaviate/Verba
Aug 2023 - Mar 2025
Retrieval Augmented Generation (RAG) chatbot powered by Weaviate
Contributions:8 releases, 34 reviews, 62 PRs in 1 year 7 months
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