Derek Osgood
VP Agents & Orchestration
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDerek Osgood is a product and GTM executive with 9 years of experience building and scaling startups, hypergrowth scaleups, and global brands from PlayStation to ZoomInfo, where he now leads the agentic platform product team. He founded and exited two AI-first companies—Ignition and DoubleO.ai—bringing deep practical experience in agent orchestration, promptless workflow construction, and embedded real-time evaluation for reliable automation. Equally fluent in product, growth, and marketing, Derek has driven double-digit monthly growth, owned P&Ls, and led go-to-market strategies that turn technical innovations into outsized revenue. His technical chops include hands-on NLP and deep learning work (contributions to popular reinforcement learning and deep NLP repos), giving him credibility with engineering teams. Based in San Francisco, he also advises and invests through GTMfund, combining operator instincts with early-stage investment judgment. Colleagues describe him as a results-oriented builder who prioritizes delightful customer experiences and is, quietly, a good human to work with.
9 years of coding experience
9 years of employment as a software developer
B.S. Marketing Entrepreneurship, B.S. Marketing Entrepreneurship at USC Marshall School of Business
English
Github Skills (9)
openai-gym10
lstm10
tensorflow10
python10
reinforcement-learning10
machine-learning9
nlp9
numpy8
jupyter-notebook7
Programming languages (2)
Github contributions (5)
rockingdingo/deepnlp
Oct 2016 - Jan 2018
Deep Learning NLP Pipeline implemented on Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:54 commits, 89 pushes, 6 branches in 1 year 2 months
Contributions summary:Derek contributed significantly to the development of a POS (Part-of-Speech) tagging model using LSTM and Bi-LSTM architectures. Their work involved implementing model configurations, defining training and evaluation functions, and integrating the model with the project's data processing pipeline. Furthermore, they added support for multiple languages (Chinese and English), demonstrating a focus on model versatility and adaptability.
nlppipelineword-embeddingsdeep-learningnlp-deep-learning
dennybritz/reinforcement-learning
Jan 2017 - Feb 2017
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 1 PR, 3 comments in 9 days
Contributions summary:Derek primarily focused on debugging and improving reinforcement learning algorithms implemented in the repository. Their contributions included fixing version incompatibility issues with TensorFlow and gym, ensuring the code was compatible with updated versions of the libraries. They updated the gym.wrappers.Monitor to record videos, and added other changes to deep Q learning solution code.
pythonmultiagent-reinforcement-learningdeep-reinforcement-learningtensorflowreinforcement
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