Aleš Tamchyna
Principal AI Researcher at Phrase
Prague, Czechia
Summary
🤩
Rockstar🎓
Top SchoolAleš Tamchyna is a Principal AI Researcher with 14 years of experience building production-grade NLP and machine translation systems, currently leading AI research at Phrase where he helped create the company's ML capabilities from scratch. He specializes in deploying LLM-driven solutions and generative AI at scale, with hands-on expertise in model fine-tuning, MCP integration, and the full lifecycle from research prototypes to global production services. Aleš previously architected Phrase NextMT—translating billions of words monthly for major tech customers—and maintains an active research profile with publications in ACL, EMNLP and WMT and a US patent in automatic text classification and translation. His deep academic roots (Ph.D. in Computational Linguistics) are complemented by practical open-source contributions to foundational tools like NLTK and Moses, where he improved evaluation accuracy and automated build/test workflows. Colocated in Prague, he combines rigorous linguistic research with pragmatic engineering to make cutting-edge translation and LLM tech reliably usable in real-world products.
14 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computational Linguistics, Doctor of Philosophy (Ph.D.), Computational Linguistics at Charles University in Prague
Czech, English, German, Russian
Github Skills (13)
algorithm10
nltk10
git10
algorithms10
bash10
python10
natural-language-processing10
build-automation10
cicd10
testing10
machine-learning9
unit-testing8
unit-test8
Programming languages (7)
Github contributions (5)
moses-smt/mosesdecoder
Sep 2011 - Mar 2017
Moses, the machine translation system
Role in this project:
DevOps Engineer
DevOps EngineerContributions:473 commits, 131 pushes, 2 branches in 5 years 7 months
Contributions summary:Aleš focused on improving the testing and build processes of the project. They added and modified scripts to automate testing procedures for new commits, including regression testing. The user also introduced features to download and integrate regression test data, download and compile external dependencies, and made changes to simplify the build process. Their work primarily involved the automated testing infrastructure and the configuration of the build process.
i18nmachine-translationtranslationlocalizationmoses
nltk/nltk
Jun 2018 - Aug 2018
NLTK Source
Role in this project:Back-end Developer
Back-end DeveloperContributions:12 commits, 1 PR, 15 comments in 1 month
Contributions summary:Aleš primarily contributed to the `nltk/translate/chrf_score.py` module, implementing and refining the ChrF score calculation. Their work included fixing potential division-by-zero errors, aligning the implementation with Maja Popovic's outputs, and incorporating features like whitespace and beta handling. These changes demonstrate a focus on accuracy and feature enhancements within the context of natural language processing evaluation.
nlppythonmachine-learningnltknatural-language-processing
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