Christopher Lennan
Staff Engineer at idealo internet GmbH
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolChristopher Lennan is a Staff Engineer based in Berlin with nine years of experience building production ML systems and leading teams to deliver measurable business value. He progressed through technical and leadership roles at idealo, moving from Machine Learning Engineer to Tech Lead and now Staff Engineer, combining hands-on model work with people and process leadership. His contributions to open-source projects like image-quality-assessment and imagededup show practical expertise in CNNs, model training, deployment, and backend refactoring, including test-driven improvements to image encoding and duplicate detection. Trained in statistics and economics, he blends rigorous quantitative thinking with product-focused engineering decisions. He’s known for empowering development teams to ship robust ML features and for translating research-grade models into maintainable production services. An understated strength is his track record of improving developer workflows and reproducibility through careful refactors and tooling.
9 years of coding experience
10 years of employment as a software developer
Master's degree, Statistics, Master's degree, Statistics at Humboldt-Universität zu BerlinMaster's degree, Economics, Master's degree, Economics at University of Warwick
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
mask-rcnn10
faster-rcnn10
computer-vision10
machine-learning10
deeplearning-ai10
keras10
convolutional-neural-networks10
deep-learning10
tensorflow10
python10
numpy9
pytorch9
docker8
dockers8
aws8
Programming languages (2)
Github contributions (5)
idealo/image-quality-assessment
Jun 2018 - Nov 2019
Convolutional Neural Networks to predict the aesthetic and technical quality of images.
Role in this project:
ML Engineer
ML EngineerContributions:59 commits, 6 PRs, 8 pushes in 1 year 5 months
Contributions summary:Christopher made significant contributions to the image quality assessment project, focusing on model building, training, and evaluation. They implemented a prediction script and refactored the existing code to integrate with a new prediction script. Further contributions include adding label files for training and testing, configuring environment variables within the Docker container, and refactoring code, suggesting a hands-on role in the model development and deployment process.
deep-learningunet-image-segmentationinpaintingconvolutionalmachine-learning
idealo/imagededup
Aug 2019 - Oct 2022
😎 Finding duplicate images made easy!
Role in this project:Back-end Developer
Back-end DeveloperContributions:26 commits, 7 PRs, 11 pushes in 3 years 2 months
Contributions summary:Christopher primarily focused on refactoring and improving the `CNN` class within the `imagededup` repository. Their commits involved significant changes to the class's structure, including updates to the `DataGenerator` class, and incorporating methods for encoding images and finding duplicates. They also added tests to the `DataGenerator` and `CNN` methods to ensure code correctness and functionality.
hashingpytorchpythonduplicatefinding
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