Roberto Esposito
Research Engineer at Weaviate
Amsterdam, North Holland, Netherlands
Summary
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Rockstar🎓
Top SchoolRoberto Esposito is a Research Engineer at Weaviate in Amsterdam with four years of experience building ML-driven back-end systems and vector search infrastructure. He holds an MSc in Computer Science from the University of Pisa (110/110 with honors) and works across Go, Rust, Python, and C. At Weaviate he has contributed to the open-source vector database by improving batch and object metrics, implementing object memory estimation, fixing a deleted-node search bug, and adding ColPali/ColBERT support. Prior roles include low-latency (≈2 ms) communication protocol development at Leonardo and research work in deep learning, reinforcement learning, and information retrieval, reflecting a blend of research rigor and production-focused engineering.
5 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Computer Science, 110 with honors, Bachelor's degree, Computer Science, 110 with honors at Università degli Studi di SalernoMaster's degree, Computer Science, 110 with honors, Master's degree, Computer Science, 110 with honors at Università di Pisa
Italian, English, Spanish
Github Skills (6)
vector-search10
vector-database10
go10
metric10
testing9
hnswlib9
Programming languages (7)
Github contributions (5)
weaviate/weaviate
Sep 2023 - Apr 2025
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:23 reviews, 46 PRs, 156 pushes in 1 year 6 months
Contributions summary:Roberto primarily contributed to adding new metrics, and enhancing the existing ones, for batch sizes and object processing in Weaviate. They also added functionality related to a fixed seed vector generator function and modified existing testing helpers. The user further implemented code that estimates the object memory consumption and fixed a bug that would arise from a deleted node during a search. These changes focus on improving data handling, memory management, and testing within the vector database.
approximate-nearest-neighbor-searchsemantic-search-enginehnswfaultsimilarity-search
robbespo00/information-retrieval-notes
Dec 2021 - Jan 2022
Contributions:10 commits, 8 pushes, 1 branch in 25 days
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