Martin Malmsten is a data scientist and seasoned IT architect with 20 years’ experience building production-ready ML and data platforms at the National Library of Sweden. He blends deep machine learning and MLOps expertise with infrastructure skills—Kubernetes, semantic web, and enterprise systems—to move models from research into reliable services. A practical engineer who started as a UNIX sysadmin and developer, he has led search and development teams while shaping creative technical environments. Martin is also an active open-source contributor to Hugging Face Transformers, adding Albert support to the NER pipeline, showing his ability to adapt state-of-the-art NLP models to real-world tooling. Based in Greater Stockholm, he pairs institutional knowledge of national-scale library systems with modern AI infrastructure practices.
20 years of coding experience
14 years of employment as a software developer
Computer Science and Technology, Computer Science and Technology at Linköping University
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Role in this project:
ML Engineer
Contributions:9 commits, 4 PRs, 4 comments in 25 days
Contributions summary:Martin contributed significantly to the `transformers` repository, specifically focusing on adding support for the Albert model within the Named-Entity-Recognition (NER) pipeline. This involved implementing the `AlbertForTokenClassification` class, integrating Albert into the existing NER framework, and modifying code to handle Albert's specific tokenization requirements. Further contributions included test implementations and minor code corrections, demonstrating expertise in adapting and extending the library's capabilities for different model architectures.
Contributions:59 commits, 1 PR, 24 pushes in 1 year 8 months
lab
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