Matt Fleming is a Principal Systems Engineer with 26 years of deep systems and open-source experience, currently leading performance-critical engineering at Cloudflare. He has a long track record of bootstrapping and maintaining kernel and EFI subsystems, contributing to projects from Apache Cassandra to the high-performance Redpanda streaming platform. Comfortable both as an individual contributor and as a manager, he has led global teams delivering major OSS features (SAI, UCS, trie-indexed SSTables) and driven performance investigations, microbenchmarks, and test automation. His background spans low-level embedded work, kernel page-table and EFI runtime fixes, and distributed database performance—rarely found together at this depth. An active standards participant and writer, he pairs practical systems hacking with advocacy through papers, talks and technical articles. Based in Calderdale, England, he also co-founded a performance platform, demonstrating entrepreneurial initiative alongside enterprise impact.
26 years of coding experience
17 years of employment as a software developer
Leeds College of Technology
BSc Computer Science, BSc Computer Science at University of Leeds
Contributions summary:Matt primarily contributed to the low-level aspects of the `mpc5200` repository, specifically focusing on the "sh" architecture. Their work involved modifying inline assembly constraints and the atomic functions to improve the handling of interrupts, making the code safe for irqsoff tracing. They also fixed compiler errors and addressed issues related to the ftrace implementation within the context of the SH architecture.
Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
Role in this project:
Backend Developer & Performance Engineer
Contributions:12 reviews, 14 PRs, 2 pushes in 3 months
Contributions summary:Matt primarily focused on enhancing the Redpanda streaming data platform's core functionalities, specifically within the backend code. Their contributions involved improving memory management accuracy by introducing byte-granular node memory size checks, which optimized partition allocation. Furthermore, the user implemented microbenchmarks for produce operations and refined existing tests, including the many_partitions_test, to stress partition limits, indicating a focus on performance and scalability. Additionally, the user added error messages to timeout scenarios for easier debugging in tests.
realtimezookeeperdata-platformkafka-api10x
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