Richard Kuo is a founding engineer and seasoned technology leader with 12 years of hands-on experience building scalable media and cloud systems from startups to large platforms. He has led core engineering at imgix—designing GPU-accelerated rendering, streaming encoding pipelines, and a high-throughput image/video processing stack that scaled to billions of daily images—and previously ran large video encoding and QC operations at Netflix. Richard combines deep systems and backend expertise (C/C++, Python, Objective-C, Lua) with product and operational chops from roles at Google/YouTube and as a multiple-time founder/CTO. More recently he contributes backend fixes and observability improvements to gen-AI collaboration tooling like Onyx, bridging AI with team knowledge systems. Known for pragmatic architecture decisions—migrations to CDN/HTTP, cloud replatforming, and production regression testing—he excels at turning research-grade ideas into reliable production services. Based in San Francisco, he pairs an EE degree and Wharton entrepreneurial training with a track record of shipping complex media infrastructure.
12 years of coding experience
23 years of employment as a software developer
Bachelor's Degree Entrepreneurial Management, Bachelor's Degree Entrepreneurial Management at The Wharton School
High School, High School at St. John's School
Bachelor's Degree Electrical Engineering, Bachelor's Degree Electrical Engineering at University of Pennsylvania
Open Source AI Platform - AI Chat with advanced features that works with every LLM
Role in this project:
Backend Developer
Contributions:3 reviews, 3 comments in 6 months
Contributions summary:Richard's contributions primarily involve backend development, focusing on fixing bugs and adding features to the Onyx application. They made changes to the Vespa index, Gmail connector and related testing, and configurations. Their work demonstrates an understanding of the application's internal workings, including components related to data indexing, email connectors, and testing infrastructure. The user also made changes to the monitoring and logging infrastructure to ensure better observability.
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