Simba Khadder
Context Engine at featureform
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSimba Khadder is a Silicon Valley engineer-founder with 6 years of experience building data infrastructure and ML platforms, now leading Context Engine at Redis after Featureform’s acquisition. He co-founded and scaled Featureform—raising $8M and open-sourcing core products—to standardize feature engineering and serving for ML, and previously ran ML for personalization at Triton serving 100M+ MAU. Hands-on across systems and languages, Simba has implemented low-level backend components like a C++ gRPC embedding store and added approximate nearest neighbor search (hnswlib) to a virtual feature store. Comfortable shipping both infrastructure and product, he combines entrepreneurial grit with production-grade engineering for real-time AI data delivery. Based in San Francisco, he brings a blend of startup leadership and deep systems experience that bridges research ideas to operational ML services.
6 years of coding experience
6 years of employment as a software developer
University of California, BerkeleyUniversity of California Santa Cruz
Stackoverflow
9reputation
796reached
0answers
2questions
Github Skills (14)
feature-store10
c-language10
embedding10
cprogramming-language10
grpc10
python9
vector-database9
machine-learning8
event-driven6
assembly6
event-handling6
x866
networking6
masm6
Programming languages (2)
Github contributions (5)
featureform/featureform
Oct 2020 - Nov 2022
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:606 reviews, 353 commits, 212 PRs in 2 years 1 month
Contributions summary:Simba focused on extending the functionality of a virtual feature store. They implemented the initial embedding store using unordered maps, designed a gRPC proto for the embedding store, and created a C++ gRPC server and a Python client for interaction. Additionally, the user added approximate nearest neighbor functionality using hnswlib. The contributions involved building core components and infrastructure for the feature store's backend.
pythondata-qualitydata-scienceembeddingsinfrastructure
streamsql-io/streamsql
May 2020 - Aug 2020
StreamSQL is a feature store for machine learning.
Contributions:230 commits, 40 PRs, 90 pushes in 3 months
feature-storestreamsqldatabasemachine-learning
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