Jack Eadie is a Senior Software Engineer based in Queensland, Australia with nine years of experience building scalable data, ML and cloud-native systems across startups and enterprise teams. He has led engineering and cloud control-plane work at Marqo—designing the Marqo Cloud control plane and reimplementing core data-plane services for performance—and now focuses on data & AI infrastructure for Web3 at Spice AI. His background spans production ML tooling, distributed ETL and observability at Amazon, to regulated medical AI infrastructure and research-grade image processing pipelines. An active open-source contributor, Jack has improved embedding search and image-handling in the notable Marqo project and helped refine ANN benchmarking workflows, showing strength in both backend systems and ML engineering. He combines hands-on coding with systems design, often unblocking customers quickly (onboarding Marqo Cloud in his first week) and optimizing cost, performance and reliability across complex stacks.
9 years of coding experience
7 years of employment as a software developer
High School, ATAR: 99.80, High School, ATAR: 99.80 at Brisbane Grammar School
Dalian Neusoft University of Information
Bachelor’s Degree, Software Engineering/ Mathematics, GPA: 6.88, Bachelor’s Degree, Software Engineering/ Mathematics, GPA: 6.88 at The University of Queensland
Exchange Semester, Exchange Semester at University of Waterloo
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Role in this project:
Back-end Developer & ML Engineer
Contributions:153 reviews, 14 commits, 33 PRs in 1 month
Contributions summary:Jack primarily contributed to the backend functionality of the Marqo search engine, specifically focusing on document indexing and tensor generation. They implemented features to handle non-tensor fields within documents during indexing, as well as optimized and fixed bugs related to parallel processing of document additions. Additionally, the user worked on improving the handling of image loading from URLs and integrated these features into the search engine. These changes are focused around improving the functionality of adding documents and processing images to enhance the user search experience.
Benchmarks of approximate nearest neighbor libraries in Python
Role in this project:
Back-end Developer
Contributions:1 review, 4 PRs, 7 comments in 7 months
Contributions summary:Jack performed restructuring tasks on the project. The primary focus was on modifying multiple module files, including algorithms for various nearest neighbor libraries such as Qdrant, Lucene, and Faiss. This involved updating imports, modifying class structures, and adjusting parameters, indicating involvement in the core functionality of the benchmarking algorithms. The user also updated the configuration files and resolved merge conflicts.
pythonnearest-neighbornearestdockerbenchmark
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