Samuel Läubli
CEO at Supertext
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolSamuel Läubli is a machine translation expert and product-focused engineering leader with 12 years of experience building MT systems, academic research, and startups; he co-founded TextShuttle and now leads Supertext as CEO. He holds a summa cum laude PhD in Machine Translation and an MSc in AI from Edinburgh, and has bridged research and industry roles from Autodesk to Lilt and ZHAW. Technically hands-on, he contributed to prominent open-source MT work like Nematus, improving sentence-level BLEU scoring and evaluation robustness. Samuel combines deep knowledge of human–computer interaction and cognitive translation modeling with operational experience shipping production MT for post-editing and end users. His background shows a rare blend of academic rigor, product delivery, and metric-driven improvements to translation quality.
12 years of coding experience
9 years of employment as a software developer
Master of Science (M.Sc.), Artificial Intelligence, with Distinction, Master of Science (M.Sc.), Artificial Intelligence, with Distinction at The University of EdinburghDoctor of Philosophy (Ph.D.), Machine Translation, summa cum laude, Doctor of Philosophy (Ph.D.), Machine Translation, summa cum laude at University of Zurich
German, German, English, French, Spanish
Stackoverflow
1,426reputation
122kreached
6answers
12questions
Github Skills (15)
sequence-to-sequence10
machine-translation10
neural-machine-translation10
python10
tensorflow9
nlp9
multiprocessing6
algorithm6
namespaces6
ggplot6
regex6
reservoir-sampling6
rdflib6
scikit4
scikit-learn4
Programming languages (4)
Github contributions (5)
EdinburghNLP/nematus
Sep 2016 - Aug 2017
Open-Source Neural Machine Translation in Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:30 commits, 2 PRs, 17 pushes in 11 months
Contributions summary:Samuel primarily focused on integrating sentence-level BLEU scoring into the neural machine translation framework. They refactored the scoring process by creating separate classes for the scorer and reference. The user implemented score interpolation, improved the BLEU calculation by adding clipping, and incorporated a scorer provider. These changes indicate a focus on improving the evaluation metrics for the machine translation model.
machine-translationtranslationnmttensorflowsequence-to-sequence
laeubli/segcats
May 2014 - Aug 2014
Contributions:100 commits, 1 push in 2 months
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