Johannes Messner
Founding ML Engineer at Tabula
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolJohannes Messner is a Founding ML Engineer based in Berlin with seven years of hands-on experience building and shipping multimodal AI systems and developer tooling. He blends academic training from LMU, ETH Zürich, and Oxford with startup grit—co-founding a YC company and leading ML work at Jina AI where he was a core maintainer of DocArray, a widely used library for multimodal data under the Linux Foundation AI&Data. His work spans model fine-tuning, retrieval-augmented generation, backend engineering, and cloud-native deployment (gRPC, Kubernetes), and he has contributed substantive documentation and core schema work to DocArray and routing/flow features in jina-ai/serve. Currently automating accounting at Tabula after researching LLM training at Aleph Alpha, he combines product-minded engineering with deep technical expertise in data schemas and vector-backed retrieval. Colleagues would call him a pragmatic researcher-engineer who moves quickly from prototypes to production-grade infrastructure.
7 years of coding experience
4 years of employment as a software developer
MSc Computer Science, MSc Computer Science at University of OxfordBachelor of Science Computer Science and Politics, Bachelor of Science Computer Science and Politics at Ludwig-Maximilians-Universität MünchenVisiting Student Computer Science, Visiting Student Computer Science at ETH Zürich
German, English, Italian
Github Skills (25)
unit-testing10
fastapi10
python10
api-documentation10
document-processing10
data-structure9
kubernetes9
dockers9
docker9
kubernetes-pods9
protobuffer9
protobuf9
grpc9
data-structures9
microservices8
Programming languages (7)
Github contributions (5)
docarray/docarray
Jul 2022 - Jan 2023
Represent, send, store and search multimodal data
Role in this project:
Backend Developer
Backend DeveloperContributions:630 reviews, 190 commits, 201 PRs in 6 months
Contributions summary:Johannes's commits primarily focus on enhancing the documentation and core functionalities of the DocArray library. Their contributions include creating and refining API references, which involved modifications across several Python files, including `docarray/array/document.py`, `docarray/document/__init__.py`, and various storage-related files. The user demonstrates proficiency in defining and managing the structure of multimodal document schemas. The user also made changes involving the support for different tensor types.
protobuffeature-storevectorunstructured-dataqdrant
jina-ai/serve
Jan 2022 - Jan 2023
☁️ Build multimodal AI applications with cloud-native stack
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:283 reviews, 95 commits, 179 PRs in 1 year
Contributions summary:Johannes primarily contributed to the backend functionality of the `jina-ai/serve` repository. They focused on implementing features related to document processing and routing, specifically enhancing the flow post capabilities. They also addressed code refactoring tasks, including the removal of replica IDs. Furthermore, the user implemented tests to validate the functionality and integration aspects of the code.
opentelemetryllm-inferencecreative-aigrpcproduction
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