Daniel Severo
Montreal, Quebec, Canada
Summary
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SeniorDaniel Severo is a Senior Research Engineer at Meta's Superintelligence Labs (FAIR) with a decade of experience bridging hardware, embedded firmware, and state-of-the-art generative modeling research. He earned a Ph.D. from the University of Toronto and the Vector Institute in 2024 studying generative modeling and information theory, and spent much of his graduate work interning at Meta and Google AI on large-scale pre-training and inference for autoregressive, diffusion, and flow-matching models. Prior roles span machine learning in healthcare and recommendation systems, plus early career embedded systems and signal-processing projects, giving him a rare cross-disciplinary fluency from microcontrollers to multi-GPU model scaling. He publishes and contributes research (including work on compression and perceptual modeling) and brings practical production experience from building ML systems in clinical and e-commerce settings. Notably, his trajectory reflects both deep theoretical chops and hands-on engineering—optimizing compute at inference time as well as training—making him adept at turning research advances into scalable systems.
10 years of coding experience
Github Skills (127)
python10
parallel-computing10
streamlit10
pydata10
pandas10
numpy10
scipy10
data-science10
data-structure10
dask10
data-analysis10
scikit-learn10
data-structures10
r9
latex9
Programming languages (11)
Github contributions (5)
3778/COVID-19
Mar 2020 - Jun 2020
Ciência de Dados aplicada à pandemia do novo coronavírus.
Contributions:184 commits, 36 PRs, 127 pushes in 3 months
pandemiapythondados
dsevero/A-Report-on-the-Ziggurat-Method
May 2019 - Nov 2019
Contributions:1 release, 33 commits, 24 pushes in 6 months
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