Mark Levy
ML Manager at Apple
England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMark Levy is an ML Manager with 13 years of experience building high-performing teams and shipping applied AI across music products at Apple, where he progressed from applied researcher to leading personalization and music-related ML research. His expertise spans recommender systems, audio analysis, big data processing, and Python/Java development, grounded in a PhD in Computer Science and a unique academic background in mathematics and music. He has led production services at Last.fm and Mendeley-era recommender work, and contributes practical documentation to open-source recommender tooling to make complex systems more accessible. Known for bridging research and engineering, he focuses on turning innovative ML research into usable, product-ready features that improve listening and creation experiences.
13 years of coding experience
19 years of employment as a software developer
BA, Mathematics, Music, BA, Mathematics, Music at University of CambridgeMSc, Computer Science, MSc, Computer Science at Birkbeck, University of LondonMMus, Musicology, MMus, Musicology at King's College LondonDoctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Queen Mary, U. of London
Github Skills (5)
rs10
restructuredtext10
sphinx10
documentation10
python5
Programming languages (1)
Github contributions (5)
Mendeley/mrec
Sep 2013 - Dec 2013
A recommender systems development and evaluation package by Mendeley
Role in this project:
Technical Writer
Technical WriterContributions:68 commits in 2 months
Contributions summary:Mark's commits primarily focus on documentation updates and enhancements within the `mendeley/mrec` repository. Their contributions involve refining existing documentation files, adding detailed explanations of features, and incorporating examples to improve usability. They restructured the documentation by adding new sections and modules, such as examples and preparation guides, making the overall package easier to understand and use. The commit messages indicate a strong emphasis on creating usable and accessible documentation for other users of the recommender systems library.
pythonrecommenderevaluationmachine-learningrecommender-systems
gamboviol/bpr
May 2013 - Jun 2013
Contributions:12 commits in 9 days
rankingbayesianbayesian-personalized-rankingpersonalized
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.