Summary
Matthew Mccallum is a Staff Machine Learning Researcher and PhD-trained signal processing expert with eight years of industry experience building state-of-the-art audio and music ML systems. He led development of large-scale supervised and self-supervised music foundation models trained on 200k+ hours, productionized cross-framework ML infrastructure that cut deployment cycles from months to days and saved $2.5M/year, and shipped embedding-based fingerprinting and generative audio tooling. His work spans research, engineering and productization—from real-time 2GB/s training pipelines and high-throughput inference for millions of tracks, to controllable latent-space methods for pitch and tempo manipulation. Based in London, he combines deep C++/Python engineering roots with a track record of award-winning demo apps and infrastructure used by multi-disciplinary teams. Notably, he has repeatedly translated novel signal-processing research into scalable, cost-saving systems for music companies.
8 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (PhD) Signal Processing, Doctor of Philosophy (PhD) Signal Processing at University of Auckland