Daniel Hussey
Applied Scientist II at Amazon
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolDaniel Hussey is an Applied Scientist II based in Berlin with 11 years of experience specializing in applied NLP and machine translation, currently driving MT research and production at Amazon. He focuses on Transformer efficiency and scaling, multilingual models, and controlled text generation while balancing rigorous research with production-quality code and architecture. His open-source contributions to AWS Sockeye—adding beam search history storage, batched top-k, and Monte Carlo dropout at inference—reflect a strong emphasis on inference behavior and analysis at scale. With a Master’s in Language Technology and a background in translation, he combines linguistic insight with deep learning engineering to solve real customer-facing quality and scalability problems.
12 years of coding experience
Degree in Translation and Interpreting, Translation and Interpreting, Award for the Bachelor's highest grade point average (Grade: 9,01/10), Degree in Translation and Interpreting, Translation and Interpreting, Award for the Bachelor's highest grade point average (Grade: 9,01/10) at Universitat Pompeu FabraMaster in Language Technology, Computational Linguistics, Master in Language Technology, Computational Linguistics at University of GothenburgErasmus Programme, Langues Étrangères Appliquées, Erasmus Programme, Langues Étrangères Appliquées at Université Lumière (Lyon II)
Spanish, Catalan, English, French, German, Swedish
Stackoverflow
3reputation
713reached
0answers
1question
Github Skills (11)
attention-mechanism10
beam-search10
machine-translation10
sequence-to-sequence10
pytorch10
deep-learning10
inference10
python9
pandas6
transformer3
tensorflow3
Programming languages (1)
Github contributions (5)
awslabs/sockeye
Feb 2018 - Jul 2020
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 10 PRs, 32 comments in 2 years 5 months
Contributions summary:Daniel contributed to the Sockeye framework, focusing on enhancements related to beam search and model behavior during inference. They added the functionality to store beam search histories for analysis, including modifications to `inference.py` and related classes. Furthermore, they implemented a batched top-k implementation and integrated Monte Carlo dropout during inference.
neural-machine-translationpytorchsequence-to-sequencedeep-learningdeep-neural-networks
Deseaus/GF
Mar 2015 - Jul 2015
Contributions:28 commits, 9 pushes, 1 branch in 4 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.