Dominik Jain
Co-Founder at Oraios AI
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolDominik Jain is a co-founder and AI researcher with 19 years of experience building production-ready ML systems and research-grade algorithms from Munich. He blends deep academic credentials—a summa cum laude doctorate in AI from TUM—with hands-on software architecture, having led applied research and engineering teams across industry and academia. His work spans reinforcement learning, generative AI, Graph-SLAM and probabilistic mobility models, and he has contributed to notable open-source projects including the Tianshou RL library and the g2o graph optimization framework. Dominik is comfortable moving between low-level backend improvements (e.g., algorithm integrations, model persistence, logging) and high-level research, producing both engineering artifacts and scientific publications. As a founder at Oraios AI and former lead researcher at appliedAI, he focuses on turning cutting-edge research into reusable libraries and training material for practitioners. Colleagues describe him as someone who systematically removes technical debt while elevating the reproducibility and robustness of ML systems.
19 years of coding experience
2 years of employment as a software developer
Dr. rer. nat., Informatik (künstliche Intelligenz), summa cum laude, Dr. rer. nat., Informatik (künstliche Intelligenz), summa cum laude at Technische Universität München (Technical University of Munich)Certificate, Technology Management, Certificate, Technology Management at Center for Digital Technology and Management (CDTM)
Github Skills (20)
pytorch10
c-language10
python10
back-end-development10
api-design10
graph10
reinforcement-learning10
deep-learning10
optimisation10
cpp10
cprogramming-language10
slam10
optimization10
g2o10
eigen8
Programming languages (9)
Github contributions (5)
thu-ml/tianshou
Sep 2023 - Mar 2025
An elegant PyTorch deep reinforcement learning library.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:106 reviews, 13 PRs, 118 pushes in 1 year 6 months
Contributions summary:Dominik contributed to the high-level interface design of the deep reinforcement learning library, specifically implementing and refactoring agent, module, and experiment components. They integrated support for Soft Actor-Critic (SAC) and Policy Gradient (PG) algorithms, including the creation of necessary configuration classes and components. Additionally, the user refactored configuration objects, added support for model persistence, and improved the logging functionalities of the library.
deep-reinforcement-learningbenchmarknpgtd3reinforcement
RainerKuemmerle/g2o
Feb 2017 - Jan 2018
g2o: A General Framework for Graph Optimization
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 3 PRs, 2 comments in 11 months
Contributions summary:Dominik primarily contributed to improving the robustness and functionality of the g2o graph optimization framework. Their work involved fixing return codes in existing methods, and preventing undesirable fallback behavior. Furthermore, the user added new edge types for graph optimization problems, expanding the framework's capabilities in 2D and 3D SLAM applications. They also ensured the correct handling of input stream data for existing parameter types.
roboticsoptimizationc-plus-plusgraph-optimizationg2o
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