Paul Tune
Staff Research Scientist at Canva
Sydney, New South Wales, Australia
Summary
👤
Senior🎓
Top SchoolPaul Tune is a Staff Research Scientist and machine learning engineer with 11 years of experience applying statistical modeling, signal processing, and information theory to production ML systems at scale. Based in Sydney, he has led marketing optimization and recommender efforts at Canva—delivering contextual bandits, survival-analysis bidding, LLM-driven creative tooling, and value-prediction pipelines that saved millions annually. His background as an academic researcher (PhD and postdoc) informs a rigorous approach to data measurement, compressed sensing and streaming algorithms, and the design of ML systems that work under tight memory and compute constraints. Paul blends hands-on engineering—GPU-optimized vision pipelines and distributed hashing schemes—with product-focused leadership, recently moving into RL post-training and infrastructure work. Not obvious from titles: he regularly bridges theoretical performance bounds and pragmatic productionization, presenting at major venues such as ACM SIGCOMM to audiences of 400+.
11 years of coding experience
10 years of employment as a software developer
The University of Melbourne
Malay, English
Github Skills (47)
drawing10
visualization9
portable9
canvas9
machine-learning9
mxnet9
tabular9
ai9
collaboration9
modification9
whiteboard9
mutation9
sketching8
julia8
criterion8
Programming languages (5)
Github contributions (5)
ptuls/large_deviations_tech
Feb 2016 - Sep 2021
Contributions:6 pushes, 1 branch in 5 years 7 months
ptuls/multiarm_kelly_portfolio
Jan 2019 - Jan 2020
Simulating the combination of multiarm bandits with the Kelly criterion for portfolio allocation
Contributions:17 commits, 10 pushes, 1 branch in 1 year
combinationpythonfor-portfoliocriteriondata-science
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