Fabio Kepler is a seasoned AI leader and CTO with 15 years of experience building research-driven products and teams, now co-founding Spinnable to commercialize next-generation AI. He spent nearly a decade at Unbabel advancing machine translation and research, rising from associate researcher to Director of AI Research and bridging academic rigor with production deployments. Fabio holds a PhD-focused background in NLP from Universidade de São Paulo with visiting work at UPenn, and he has taught and led technology transfer at Brazilian universities, underscoring his ability to translate research into real-world impact. An active contributor to ML tooling—having tidied and improved a Lisbon Machine Learning Summer School NLP toolkit—he blends hands-on engineering (Python/ML) with strategic product vision. Based in Lisbon, he combines academic depth, startup execution, and a knack for making complex NLP systems maintainable and production-ready.
15 years of coding experience
14 years of employment as a software developer
BSc, Computer Science, Image Processing, Generic Programming, Parallel Programming, BSc, Computer Science, Image Processing, Generic Programming, Parallel Programming at Universidade Federal de Santa Maria
PhD, Computer Science, Artificial Intelligence, Natural Language Processing, PhD, Computer Science, Artificial Intelligence, Natural Language Processing at USP - Universidade de São Paulo
PhD Visiting Scholar, Natural Language Processing, PhD Visiting Scholar, Natural Language Processing at University of Pennsylvania
Machine Learning applied to Natural Language Processing Toolkit used in the Lisbon Machine Learning Summer School
Role in this project:
Back-end Developer
Contributions:23 commits, 14 PRs, 3 pushes in 3 years
Contributions summary:Fabio primarily contributed to the codebase by fixing naming conventions according to PEP8 guidelines. They made changes in several Python files, including those related to dependency parsing and deep learning models like MLPs and RNNs. Additionally, the user touched upon files related to training classifiers, indicating a focus on improving code style and consistency throughout the project, which is likely a machine learning toolkit.
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