Max Layer
CTO at Manyfold Labs GmbH
Hamburg, Germany
Summary
🤩
Rockstar🎓
Top SchoolMax Layer is a CTO, entrepreneur and engineer from Hamburg with over a decade of professional experience building profitable, bootstrapped education and productivity startups while advising and investing in AI/ML ventures. He combines deep technical chops—PhD-level math, core contributions to Keras, Hyperopt and Ray documentation, and authorship of ML books—with hands-on product work shipping language learning, meditation and teacher-assist apps. As a prolific open-source contributor and former core developer on projects like Elephas (distributed Keras on Spark) and Keras, he bridges research-grade deep learning with production engineering. Max teaches data science and engineering, consults on complex ML systems, and leads Manyfold Labs to apply AI in learning technology. Notably, he prefers to build companies he wants to run and partners selectively with founders who bring an existing audience or community.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD) Mathematics, Doctor of Philosophy (PhD) Mathematics at University of HamburgDiploma Mathematics and Computer Science, Diploma Mathematics and Computer Science at RPTU Kaiserslautern-Landau
English, French, Swedish, German, Spanish
Github Skills (42)
json10
spark10
back-end-development10
machine-learning-models10
python10
sphinx10
testing10
technical-writing10
modeler10
machine-learning10
data-serialization10
modello10
refactor10
ml10
distributed-computing10
Programming languages (11)
Github contributions (5)
maxpumperla/hyperas
Feb 2016 - Jan 2023
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Role in this project:
ML Engineer
ML EngineerContributions:137 commits, 41 PRs, 92 pushes in 6 years 11 months
Contributions summary:Max primarily worked on developing and refining a Keras-based hyperparameter optimization wrapper, Hyperas, for machine learning models. They implemented an example using MNIST for testing purposes, and created an example for LSTM models. The user also refactored the code, fixed a dependency issue, and updated examples to accommodate API changes. They improved the tool with the aim of creating ensembles of models.
hyperparameterdeep-learningoptimizationhyperparametersmachine-learning
maxpumperla/betago
Apr 2016 - Dec 2020
BetaGo: AlphaGo for the masses, live on GitHub.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:218 commits, 12 PRs, 140 pushes in 4 years 9 months
Contributions summary:Max's initial contribution was setting up the project with `setup.py` and defining package dependencies. They then developed a playground notebook for interacting with the project's core logic, involving data loading and board representation. Further, the user implemented base classes for data processing and file loading, with specific implementations for seven-plane and three-plane processors. Finally, they integrated a frontend to interact with a bot.
alphago
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