Kay Hennig
Software Engineer Administrator
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolKay Hennig is a full-stack software engineer and administrator based in Berlin with seven years of hands-on experience building integrations, internal tools, and operational dashboards. At Caseking GmbH they administer multi-brand Zendesk instances and lead development of an internal dashboard that automates B-stock, RMA workflows, ERP evaluations, and supports order picking and warehouse refill strategies. Kay contributes to open-source ML tooling as an ML engineer on lucidrains/imagen-pytorch, improving data pipelines, multi-channel image support, CLI training/sampling, and adding unit tests to boost reliability. Comfortable bridging product and operations, Kay combines system integration and game-programming roots to deliver practical solutions that reduce manual work across support and logistics. Colleagues value their knack for turning messy ERP and shop systems into dependable, test-covered automation.
7 years of coding experience
3 years of employment as a software developer
Mittlerer Schulabschluss, Mittlerer Schulabschluss at Gustave Eiffel SchuleErnst Reuter SchuleHigh School Diploma, Communication and Media Studies, High School Diploma, Communication and Media Studies at Oberstufenzentrum Kommunikations-, Informations- und Medientechnik
Github Skills (16)
pytorch10
machine-learning10
artificial-intelligence10
deep-learning10
python10
cli10
pytest9
unit-test9
text-to-image9
unit-testing9
dataprep8
data-loading8
configuration-management8
data-preprocessing8
preprocess8
Programming languages (7)
Github contributions (5)
lucidrains/imagen-pytorch
Nov 2022 - Nov 2022
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 3 commits, 5 PRs in 7 days
Contributions summary:Kay primarily contributed to the implementation and refinement of the Imagen model in PyTorch. Their work involved adding and improving the CLI interface for training and sampling, including configuration management and dataset integration. Key contributions include adding support for different image channels (RGBA, LA, L), optimizing training processes, and improving the overall usability and functionality of the training pipeline. Furthermore, the user added unit tests to ensure the code's reliability and robustness.
pytorchdeep-learningtext-to-videotext-to-imageneural-network
TheFusion21/DuplexEngine
Jul 2020 - Nov 2020
Contributions:17 commits, 12 pushes, 1 branch in 3 months
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