John Trengrove
Head Of Applied Research at Weaviate
Queensland, Australia
Summary
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Rockstar🎓
Top SchoolJohn Trengrove is a Head of Applied Research with 12 years’ experience building production-grade analytics and search systems, currently leading applied research at Weaviate in Queensland, Australia. He brings a strong mathematical foundation (BSc Hons in Mathematics) to practical engineering, having progressed from developer roles to principal architect and research lead across fintech and enterprise search. At Weaviate he contributes hands-on to an open-source, cloud-native vector database—fixing complex cross-class search bugs, improving performance and metrics, and hardening schema and tombstone handling. His background includes shaping search and observability solutions at Elastic and delivering analytics at Commonwealth Bank, giving him a rare mix of enterprise discipline and open-source velocity. Colleagues would describe him as a pragmatic problem-solver who translates research ideas into robust, scalable systems and measurable product improvements.
12 years of coding experience
9 years of employment as a software developer
Bachelor of Science (BSc) Hons, Mathematics, Bachelor of Science (BSc) Hons, Mathematics at University of Canterbury
Github Skills (11)
hnswlib10
go10
vector-database10
testing9
metric9
error-handling9
rest-api8
api-rest8
restful-api8
api-design8
performance-optimization8
Programming languages (14)
Github contributions (5)
weaviate/weaviate
Sep 2022 - Jan 2023
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:143 reviews, 29 commits, 93 PRs in 4 months
Contributions summary:John primarily focused on improving the functionality and robustness of the Weaviate vector database. They addressed a bug related to cross-class object searches, implemented error handling for empty schemas, and updated dependencies such as Swagger. The user also made performance optimizations by reducing histogram sizes and contributed to fixing issues related to the interaction of near vector searches and group operations. In addition, they added code to support metrics, and improve the handling of tombstones.
approximate-nearest-neighbor-searchsemantic-search-enginehnswfaultsimilarity-search
weaviate/weaviate-benchmarking
Jun 2022 - Feb 2025
Tools for various benchmarking scenarios
Contributions:17 reviews, 57 PRs, 56 pushes in 2 years 8 months
benchmarkscenariosbenchmarking
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