Branden Chan
Senior Machine Learning Engineer at Choco
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolBranden Chan is a Senior Machine Learning Engineer based in Berlin with eight years of experience applying cutting-edge NLP and retrieval-augmented techniques to production systems. He has driven model development and deployment at deepset and ELTEMATE and now leads ML efforts at Choco, bringing a practical focus on question answering, semantic search and conversational agents. His academic background in computational, historical and sociolinguistics (Stanford and Cambridge) informs theoretically grounded solutions that bridge language science and engineering. An active open-source contributor, he improved the FARMReader and tutorials in the widely used Haystack framework to make QA training and evaluation more robust and GPU-efficient. Known for translating research awareness into production-ready pipelines, he combines deep linguistic insight with hands-on backend and full-stack ML engineering.
8 years of coding experience
7 years of employment as a software developer
Master's Degree Historical Linguistics, Master's Degree Historical Linguistics at University of CambridgeHigh School Mathematics English Latin Ancient Greek, High School Mathematics English Latin Ancient Greek at Sydney Grammar SchoolMaster of Arts - MA Computational Linguistics, Master of Arts - MA Computational Linguistics at Stanford University
English, German, Chinese, Latin, Greek, uyghur
Github Skills (14)
pytorch10
machine-learning10
nlp10
python10
question-answering10
bert9
transfer-learning9
transformers9
retrieval-augmented-generation9
language-model9
documentation8
ai8
generative-ai8
large-language-models8
Programming languages (5)
Github contributions (5)
deepset-ai/haystack
Mar 2020 - Nov 2022
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:170 reviews, 322 commits, 218 PRs in 2 years 8 months
Contributions summary:Branden focused on enhancing the functionality and usability of the FARMReader within the Haystack framework. Their commits show the implementation of training parameters, and fixing code comments. The user also made adjustments to the structure and evaluation datasets used in the tutorials. They modified training parameters, data directories, and documentation of the FARMReader, focusing on improving its overall training and evaluation procedures for question answering and related tasks.
pythonbertfinetuningproduction-readyhuggingface-transformers
deepset-ai/FARM
Jul 2019 - Feb 2021
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:13 reviews, 260 commits, 154 PRs in 1 year 6 months
Contributions summary:Branden made several contributions to the FARM project, focusing on improving the tutorial and implementing various functionalities. They improved the GPU utilization in the tutorial and updated the documentation to include a Colab link. Additionally, the user was involved in refactoring the code and fixing bugs related to the codebase's functionality, as well as implementing feature improvements to the question answering model.
bertxlnet-pytorchnatural-language-processingtransformersharvesting
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