Joel Akeret
Lead Machine Learning Engineer at University of Applied Sciences and Arts Northwestern Switzerland FHNW
Zurich, Zurich, Switzerland
Summary
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Rockstar🎓
Top SchoolJoel Akeret is a Lead Machine Learning Engineer based in Zurich with 12 years of experience building and shipping ML and data-driven products across industry and research. He combines a strong academic foundation from ETH Zürich and FHNW with practical leadership roles delivering computer vision and biometric liveness systems, speaker and biomarker audio models, and production ML pipelines. Joel has led teams to operationalize models end-to-end—designing lifecycle automation, cloud integrations, and robust testing—while also teaching advanced NLP as a lecturer. As an active contributor to open-source ML tooling, he improved a TensorFlow U-Net implementation for image segmentation, boosting accuracy and portability through architecture and cost-function enhancements. Known for bridging rigorous research with product-focused engineering, he brings both hands-on model development and strategic alignment with product and software teams.
12 years of coding experience
15 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at ZHAW Zurich University of Applied SciencesMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Applied Sciences and Arts Northwestern Switzerland FHNW
German, English, French
Github Skills (8)
neural-network10
net10
machine-learning10
image-segmentation10
convolutional-neural-networks10
tensorflow10
segmentation10
python10
Programming languages (3)
Github contributions (5)
jakeret/tf_unet
Aug 2016 - Aug 2019
Generic U-Net Tensorflow implementation for image segmentation
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 170 commits, 32 PRs in 3 years
Contributions summary:Joel primarily contributed to the implementation and improvement of a U-Net architecture for image segmentation. They introduced plotting functionality for visualizing predictions, optimized the network depth, and controlled the adaptation of image sizes. The user also migrated the project to Python 3 and made modifications to the cost function and network architecture to increase the accuracy of the model.
image-segmentationtensorflowneural-networkdeep-learning
jakeret/abcpmc
Apr 2015 - Apr 2016
Approximate Bayesian Computation Population Monte Carlo
Contributions:3 releases, 61 commits, 21 PRs in 1 year
bayesianmonte-carlo
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