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Senior Software Engineer @ Google AI Innovation & Research
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Max is a passionate Full Stack ML Engineer. He's been coding since he was five, done academic research in Deep Reinforcement Learning, and has years of experience both modeling novel or leveraging existing ML models, but also applying them to build user-facing products end-to-end, by himself, as part of, or leading teams. He blogs about applied Machine Learning, has won several Kaggle medals, volunteers as a coding teacher for refugees and has multiple open-source projects and international startup experience.
The University of Freiburg
Bachelor's Degree, Computer Science
January 1, 2010 – January 1, 2013
Senior Software Engineer, Google Research
November 1, 2021 – Present
Berlin, Germany
Software Engineer, Google Research
March 1, 2020 – November 1, 2021
Berlin, Germany
ReDI School of Digital Integration
Project Tech Lead
September 1, 2019 – December 1, 2019
Greater Munich Metropolitan Area
Privalino
Software Engineering Lead
July 1, 2017 – September 1, 2019
Candis GmbH
Software Engineer
November 1, 2015 – June 1, 2017
Berlin Metropolitan Area
Self-Employed
Freelance Software Engineer
February 1, 2015 – February 1, 2020
Anywhere
Triip Inc
Software Engineer
February 1, 2015 – October 1, 2015
Ho Chi Minh City
Edmodo
Research Engineer
January 1, 2014 – January 1, 2015
San Francisco Bay Area
Albert-Ludwigs-Universität Freiburg im Breisgau
Teaching and Research Assistant in Machine Learning and AI
January 1, 2012 – December 1, 2013
Greater Freiburg Area
Nero AG
(Assistant) QA Engineer
May 1, 2003 – September 1, 2008
Germany
Pong-RL
June 1, 2019 – August 1, 2019
Deep Reinforcement Learning with TensorFlow.js: Watch an agent learn the classic Pong arcade game right in your browser!
AI Against Humanity (NSFW!)
May 1, 2019 – June 1, 2019
AI-driven browser game similar to Cards Against Humanity. All cards were created by an AI, and also you're playing against a Neural Network that is run right in your browser!
Kaggle Silver Medal: Quora Insincere Questions competition
November 1, 2018 – February 1, 2019
In this machine learning competition, participants were asked to detect "insincerity" in a big dataset of Quora questions. I ended up 44th out of 4000 teams.
Lookie Lookie: Learning to track eye movement right in the browser
May 1, 2018 – August 1, 2018
TensorFlow recently released their TensorFlow.js JavaScript library for deep learning inside the browser. As there were almost no work/tutorials in this field as of yet, I wrote a blog post and an open-source library called Lookie Lookie. It learns to predict where the user is looking at on the screen based on webcam data.
Kaggle Silver Medal: Toxic Comment Classification Challenge (Google Jigsaw)
January 1, 2018 – March 1, 2018
Spending some time on the side to work on a complex NLP problem and get some Kaggle experience. I mostly relied on flexible Word Embeddings and Deep Recurrent Neural Networks and reached top 4% out of thousands without joining a team.
Solving math equations on the text-level with LSTMs
July 1, 2017 – September 1, 2017
A blog post detailing how to teach a neural network to solve math equations.
Edmodo Content Marketplace with personalized recommendations and rankings
August 1, 2014 – February 1, 2015
I was solely responsible for the backend of a first version of Edmodo's content marketplace for teachers, reaching about 50 million users. It was written as a Python microservice. Recommendations and custom rankings where powered by daily Hadoop batch jobs based on interaction data obtained from the Edmodo platform, combined with ElasticSearch. Deployment happened through Docker. This version made it to production and proved stable and fast under high traffic for several months, but after my leave from Edmodo was replaced with a new project.
Deep Learning research project: Playing arcade games with Deep Reinforcement Learning
March 1, 2013 – December 1, 2013
Under Prof. Martin Riedmiller (now Google DeepMind), I took to researching how Deep Learning can be combined with Reinforcement Learning to create completely autonomous learners. This included writing my thesis "Deep Fitted Q-Iteration for Learning Pong", but continued after my graduation. Meanwhile, DeepMind had been working on exactly the same task and published their results shortly after me. Theirs were much more impressive than mine and led to them being acquired by Google :)
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Machine Learning (Andrew Ng)
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including open-source contributions, Kaggle competitions, and volunteer work, indicates a strong passion for technology and a willingness to contribute beyond core job responsibilities. Their experience spans research, startups, and large tech companies (Google), suggesting adaptability to various organizational cultures. The involvement in ReDI School highlights a commitment to community and mentorship, which aligns well with a collaborative and supportive team environment. The breadth of skills and continuous learning (Coursera certifications) also points to a growth mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative through numerous personal projects and Kaggle participation. Leadership experience in technical roles and volunteer work suggests good collaboration and mentorship potential. The descriptions indicate a proactive and problem-solving mindset, suitable for dynamic ML engineering environments. Experience in startups also points to adaptability and a broad skill set.