Jacky Zhao is a Frontend Engineer based in Berkeley with nine years of experience building polished, user-focused web applications and developer tooling. He blends a CS background from UC Berkeley with an art degree to craft interfaces that balance elegant interaction design and robust engineering. At TikTok he led the frontend effort for privacy and safety portals and mentored new engineers, and he now contributes to scaling developer-facing ML deployment workflows—most notably improving BentoML’s Lambda logging, containerization, and Helm integrations. Jacky’s open-source work spans UI-heavy projects like the Quartz static-site generator and backend-adjacent MLOps improvements, reflecting a rare cross-over between distributed systems thinking and front-end craftsmanship. Outside work he draws from a background in illustration and Japanese studies, a combination that informs his empathetic approach to product UX and developer experience.
9 years of coding experience
8 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of California, Berkeley
Japanese Studies, Japanese Studies at Doshisha University
🌱 a fast, batteries-included static-site generator that transforms Markdown content into fully functional websites
Role in this project:
Front-end Developer
Contributions:4 releases, 888 reviews, 51 commits in 5 months
Contributions summary:Jacky primarily focused on enhancing the front-end components of the static site generator, "quartz." They implemented new features and fixed existing issues. The user's contributions included adding base pages, fixing dark mode functionality, adjusting styles, and integrating components such as a search bar, breadcrumbs, and table of contents.
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
Role in this project:
MLOps Engineer
Contributions:15 reviews, 17 commits, 19 PRs in 11 months
Contributions summary:Jacky's contributions focused on improving the deployment and operational aspects of the BentoML project. They implemented prediction logging for AWS Lambda deployments, refactored deployment-related code, and added documentation to improve user experience. The user also worked on integrating Alpine-based Docker images and containerization, demonstrating a focus on efficient and scalable deployment strategies. Furthermore, the user added a command for containerizing the BentoML service and integrated helm charts.
aiinferencejob-queuellmmodel-serving
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