Sander Schulhoff
Chief Executive Officer at HackAPrompt
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSander Schulhoff is a Chief Executive Officer and NLP/DRL researcher based in San Francisco with seven years of experience bridging academic research and startup productization. He co-founded and leads Learn Prompting and now heads HackAPrompt, translating prompt engineering and generative-AI research into learning platforms and tools. Technically he blends deep reinforcement learning and natural language processing—contributing to high-profile open-source projects such as OpenAI's Gym and the Farama Foundation's PettingZoo where he improved documentation, testing, and API compliance. In research roles he built a CNN+GRU model for event-camera video classification, developed infrastructure for a Diplomacy Discord bot using Discord.js, and streamlined annotation pipelines with Label Studio. He pairs hands-on engineering (from backend refactors and test automation to technical writing and documentation migrations like Sphinx→Docusaurus) with teaching experience, making him adept at turning complex research into accessible developer-facing products.
7 years of coding experience
4 years of employment as a software developer
University of Maryland
Github Skills (16)
prompt-engineering10
docusaurus10
api10
documentations10
multi-agent-reinforcement-learning10
python10
reinforcement-learning10
sphinx10
documentation10
testing10
box2d9
web-content9
pytest9
content-model9
gymnasium9
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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