Stefan Schneider
Senior AI & Software Engineer at Digitale Schiene Deutschland
Germany
Summary
🤩
Rockstar🎓
Top SchoolStefan Schneider is a Senior AI & Software Engineer based in Germany with nine years of experience bridging research and production-grade machine learning systems. Currently at Digitale Schiene Deutschland, he focuses on applying ML to real-world infrastructure problems while maintaining a strong research background evidenced by a PhD from Paderborn University. Stefan is an active open-source contributor to the prominent Ray project, improving RLlib examples and tooling for training and deploying reinforcement learning agents, including attention-based and autoregressive models. Known for combining hands-on engineering with clear documentation and CLI-focused usability improvements, he brings practical rigor to complex distributed ML pipelines. Colleagues describe him as curious and driven to learn, consistently pushing boundaries between research prototypes and robust software.
9 years of coding experience
Bachelor of Science (B.Sc.), Bachelor of Science (B.Sc.) at Paderborn UniversityBachelor of Science (B.Sc.), Bachelor of Science (B.Sc.) at University of Oklahoma
Spanish, Finnish, English, German
Stackoverflow
5,820reputation
940kreached
48answers
78questions
tensorflow
top-1%
docker
top-5%
keras
top-1%
python
top-5%
Github Skills (25)
ppp10
python10
rllib10
machine-learning10
reinforcement-learning10
keras10
tensorflow10
ray10
pytorch9
docker9
deep-learning8
deeplearning-ai8
data-science8
documentation7
nlp6
Programming languages (13)
Github contributions (5)
ray-project/ray
Jul 2020 - Nov 2021
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:7 reviews, 7 commits, 9 PRs in 1 year 4 months
Contributions summary:Stefan contributed to the RLlib examples and documentation, specifically focusing on training, saving, loading, and testing RL agents. They introduced examples demonstrating the use of RLlib with various environments and models, including attention-based models and autoregressive action distributions. Their contributions also involved fixing issues related to action masking and updating the RLlib examples to include more comprehensive CLI options, enhancing the usability and understanding of the framework. They added a mobile-env example.
pythonconsistsruntimetensorflowserving
RealVNF/coord-sim
Feb 2019 - Jun 2022
Lightweight flow-level simulator for inter-node network and service coordination (e.g., in cloud/edge computing or NFV).
Contributions:8 releases, 9 reviews, 151 commits in 3 years 4 months
pythonsimulationload-balancingsimulatorinter
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