Jack Ong
Founding Research Engineer at Prime Intellect
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJack Ong is a Founding Research Engineer in San Francisco with six years of experience building infrastructure for LLM pretraining, model serving, and multimodal machine learning. He has hands-on SRE and MLOps background from roles at MoneyLion and YTL AI Cloud and shipped GPU cloud and embedding/diffusion training work at Jina AI. A frequent open-source contributor, Jack has improved core libraries like docarray and jina-ai/serve—fixing deep-copy behavior, adding versioned docs, and hardening RPC and metrics for production stability. Comfortable across backend systems, cloud infra, and research code, he blends production reliability with active ML research (Google Scholar profile) and a hacker’s instinct for refactors that make large systems more maintainable.
6 years of coding experience
3 years of employment as a software developer
W24, W24 at Y CombinatorA-levels, A*A*AA (Physics, Mathematics, Further Mathematics, Economics), A-levels, A*A*AA (Physics, Mathematics, Further Mathematics, Economics) at Sunway CollegeBachelor's degree, Artificial Intelligence, CGPA - 3.92/4.00, Bachelor's degree, Artificial Intelligence, CGPA - 3.92/4.00 at University of Malaya
English, Chinese, Malay
Github Skills (23)
python10
testing10
refactoring10
documentation10
data-structure9
fastapi9
pytorch9
numpy9
microservices-application9
machine-learning9
grpc9
pydantic9
microservices9
data-structures9
docker8
Programming languages (8)
Github contributions (5)
docarray/docarray
Oct 2022 - Jan 2023
Represent, send, store and search multimodal data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:69 reviews, 30 commits, 30 PRs in 3 months
Contributions summary:Jack primarily focused on refactoring and enhancing the `docarray` library. Their contributions include fixing deep copy issues in array operations, ensuring data synchronization within context managers, and refactoring code to use enums for string constants. Furthermore, they added versioned documentation support and implemented variable tensor dimension handling, demonstrating a strong understanding of the library's core functionality and internal structure.
protobuffeature-storevectorunstructured-dataqdrant
jina-ai/serve
Oct 2022 - Jan 2023
☁️ Build multimodal AI applications with cloud-native stack
Role in this project:Back-end Developer
Back-end DeveloperContributions:47 reviews, 55 commits, 43 PRs in 3 months
Contributions summary:Jack primarily contributed to the documentation and refactoring of the `serve` module within the `jina-ai/serve` repository. Their work included adding versioned documentation, updating the version selector, and modifying request handler objects. They also fixed RPC errors and improved metric timer initialization, suggesting a focus on improving stability and observability within the serving infrastructure. The changes focused on core functionality and underlying code structure.
opentelemetryllm-inferencecreative-aigrpcproduction
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