Sander Schulhoff
Chief Executive Officer at InventoryQuant
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSander Schulhoff is a CEO and founder with 7 years of hands-on experience building and scaling AI products and open-source education platforms from San Francisco. He co-founded Learn Prompting—a community-driven resource with over 1.2M users and 100+ contributors—and now leads multiple ventures focused on NLP and reinforcement learning. His background blends academic NLP/DRL research at University of Maryland and Johns Hopkins with production engineering contributions to widely used RL toolkits like OpenAI Gym and PettingZoo. Technically fluent in prompting, DRL (PPO/VPG), and test automation, he has shipped RL-based pricing systems and developer-facing documentation for major repos. Notably, he pairs founder-level strategy with deep technical authorship, often moving between research prototypes and community-facing education.
7 years of coding experience
4 years of employment as a software developer
Y CombinatorBachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of Maryland
Github Skills (15)
prompt-engineering10
docusaurus10
api10
multi-agent-reinforcement-learning10
python10
reinforcement-learning10
sphinx10
documentation10
testing10
box2d9
pytest9
web-content9
content-model9
gymnasium9
mujoco8
Programming languages (16)
Github contributions (5)
trigaten/Learn_Prompting
Oct 2022 - Mar 2023
Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community
Role in this project:
Technical Writer
Technical WriterContributions:12 releases, 105 reviews, 1003 commits in 4 months
Contributions summary:Sander's contributions primarily focused on documenting the project. The initial commits involved setting up the Sphinx documentation framework with configuration files and a basic index page. Subsequent commits added content, including chain of thought, zero shot chain of thought and bibliography, and soft prompting stubs. Later commits included refactoring the documentation to use Docusaurus, updating the base URL, and expanding the page width and adding images.
large-language-modelsopenai-apillmgpt3prompt-engineering
openai/gym
Jan 2022 - Apr 2022
A toolkit for developing and comparing reinforcement learning algorithms.
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 reviews, 13 commits, 11 PRs in 2 months
Contributions summary:Sander contributed to the documentation and credits sections of the repository. They also refactored box2d-related documentation, corrected line lengths in Mujoco environments, and fixed typos. Further contributions involved the reformatting of docstrings, removal of unnecessary images, and addition of version histories to Box2D environments.
comparingreinforcement-learning-algorithmsdevelopingdeep-learningreinforcement-learning
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