Drew Kaul
Research Engineer at Google DeepMind
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDrew Kaul is a research engineer at Google DeepMind with 11 years of experience building and fine-tuning large AI systems, currently working on post-training techniques like SFT and RLHF for Gemini. He previously advanced NLP, semantic search, and ML foundations at Glean and contributed ML research and engineering at Meta, Facebook, and Scale AI, blending production-grade software skills with research depth. Drew has a strong systems background—contributing bug fixes and performance improvements to the popular open-source embedding DB Chroma—indicating a focus on robust, efficient back-end infrastructure for ML workflows. He earned an MS from Stanford and a BS in EECS from UC Berkeley, and has taught core ML and NLP courses, demonstrating both technical mastery and mentorship. Outside core engineering, he has run Toastmasters chapters, showing cultivated public-speaking and leadership abilities that complement his technical work. Based in San Francisco, Drew combines hands-on engineering, academic rigor, and open-source contribution to ship scalable AI systems.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS Electrical Engineering & Computer Science, Bachelor of Science - BS Electrical Engineering & Computer Science at University of California, BerkeleyClements High SchoolMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
English, Spanish
Github Skills (12)
api10
http-api10
embedding10
python10
apidoc10
numpy10
database-design9
rate-limiting9
rust8
postgresql8
llm7
lm7
Programming languages (4)
Github contributions (5)
chroma-core/chroma
Sep 2024 - Apr 2025
the AI-native open-source embedding database
Role in this project:
Back-end Developer
Back-end DeveloperContributions:79 reviews, 87 PRs, 146 pushes in 6 months
Contributions summary:Drew primarily focused on bug fixes and performance improvements within the Chroma database project. Their contributions include addressing issues related to query result shapes, merge logic, and embedding representation. They also worked on refactoring the HTTP API routes, converting embeddings to a numpy array representation, and implementing rate limiting and quota enforcement mechanisms. These changes suggest a focus on enhancing the robustness and efficiency of the core database functionalities and APIs.
document-retrievalembeddingsllms
momomasalas/momomasalas.github.io
Sep 2020 - Oct 2024
Contributions:30 pushes, 1 branch in 4 years 1 month
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