Christoffer Hjort
Lead Machine Learning Engineer at Novo Nordisk
Copenhagen, Capital Region of Denmark
Summary
👤
Senior🎓
Top SchoolChristoffer Hjort is a Lead Machine Learning Engineer based in Copenhagen with nine years of experience building and deploying ML solutions across healthcare and enterprise contexts. He holds a Master’s in Data Science from DTU and has progressed from developer and ML roles at IBM and KMD to technical leadership at KMD and Novo Nordisk, blending research rigor with production delivery. Proficient in Python, mathematical modeling, and big data, he has hands-on expertise in computer vision augmentations—contributing tested implementations like Cutout, RandomErasing and GridMask to the KerasCV ecosystem. He’s known for improving existing libraries through careful refactoring and test-driven integration, showing an engineer’s eye for maintainability as well as performance. Curious by nature, he combines cognitive-computing experience with an appetite for translating research ideas into client-facing, production-grade systems.
9 years of coding experience
7 years of employment as a software developer
Roskilde KatedralskoleBachelor’s Degree, Network Technologi and IT, Bachelor’s Degree, Network Technologi and IT at Danmarks Tekniske Universitet
Danish, English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (6)
keras10
computer-vision10
machine-learning10
image-augmentation10
tensorflow10
python10
Programming languages (2)
Github contributions (5)
keras-team/keras-cv
Jan 2022 - May 2022
Industry-strength Computer Vision workflows with Keras
Role in this project:
ML Engineer
ML EngineerContributions:12 reviews, 9 commits, 8 PRs in 3 months
Contributions summary:Christoffer primarily focused on implementing and refining preprocessing layers for computer vision tasks within the KerasCV library. Their contributions included the implementation of Cutout and RandomErasing layers, as well as the RandomChoice layer. The user also made significant improvements to existing layers like CutMix and RandomCutout, and integrated GridMask augmentation with BaseImageAugmentationLayer, demonstrating a strong understanding of image augmentation techniques. These changes involved code refactoring, adding tests, and ensuring proper integration with existing KerasCV components.
visiondeep-learningcomputer-visionindustrystrength
chjort/keras-cv
Jan 2022 - May 2022
Industry-strength Computer Vision workflows with Keras
Contributions:106 pushes, 24 branches in 3 months
visiondeep-learningcomputer-visionindustrystrength
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