Daniel Killebrew is a seasoned software engineer with 12 years of experience specializing in machine learning hardware and software co-design, currently driving ML hardware efforts at OpenAI. He has led numerical correctness, portability, and compiler re-architecture initiatives for Edge TPU at Google and built high-performance CPU kernels and numerically complex hardware blocks at Cruise. His background includes low-level, performance-critical work on Anton at D. E. Shaw Research and backend systems at DNAnexus, reflecting deep expertise across ASIC/accelerator design, compilers, and systems software. An active contributor to open-source D projects, he improved networking robustness and added MongoDB SSL support in vibe.d, evidencing practical systems and cryptography awareness. Daniel combines rigorous EECS training from UC Berkeley with a track record of translating numerical theory into production-grade hardware and software implementations. He’s drawn to novel, challenging problems at the intersection of ML algorithms, compilers, and accelerator microarchitecture.
12 years of coding experience
17 years of employment as a software developer
Master’s Degree Electrical Engineering and Computer Science, Master’s Degree Electrical Engineering and Computer Science at University of California, Berkeley
Contributions summary:Daniel primarily contributed to the vibe.d project by implementing and modifying core backend functionalities. They added a `logFatal` function to the logging system and addressed issues related to HTTP client behavior, specifically managing keep-alive connections. The user also introduced SSL support to the MongoDB driver. The user demonstrated expertise in D programming and networking, focusing on improving the robustness and features of the library.
Rust implementation of the DSLX language frontend.
Contributions:35 reviews, 13 PRs, 58 pushes in 1 year
frontendrust
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