Felix Riedel is a Senior Software Engineer based in London with 13 years of experience building performant systems and intelligent data-analysis tools. At DeepMind since 2018 he combines research-grade rigor with pragmatic engineering to deliver scalable software across languages and platforms. A true all-rounder, he has deep experience in data mining and optimizing runtime behavior—evidenced by performance-focused contributions to the Janet dynamic language and bytecode VM, where he improved hashing and sorting to avoid worst-case scenarios. Trained in computer science at KIT, he blends strong academic foundations with hands-on low-level optimization and a continual curiosity for state-of-the-art analysis techniques.
13 years of coding experience
Diplom, Computer Science, Diplom, Computer Science at Karlsruhe Institute of Technology (KIT)
Contributions:6 commits, 5 PRs, 17 comments in 12 days
Contributions summary:Felix primarily focused on improving the performance and efficiency of Janet, a dynamic language and bytecode VM. The contributions involve optimizing core functionality, specifically focusing on hashing algorithms for arrays, key-value pairs, and numbers. They made changes to the sorting algorithm (quick-sort) to prevent worst-case scenarios and experimented with performance improvements using insertion sort for smaller arrays. These modifications suggest an emphasis on improving Janet's runtime behavior.
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