Matthew Sit
Staff Software Engineer at Google DeepMind
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolMatthew Sit is a Staff Software Engineer based in San Francisco with nine years of experience building testing, performance, and debugging infrastructure for large-scale mobile applications. He progressed through multiple senior roles at YouTube where he focused on UI testing tooling and regression prevention to measurably accelerate engineering productivity, and now applies that expertise at Google DeepMind. Trained in EECS at UC Berkeley with an MSc in Machine Learning and Data Science from Imperial College, he blends systems engineering with ML fluency and a strong background in scientific programming from biotech and neuroscience apprenticeships. An experienced educator, he has lectured and led instructional teams at Berkeley, honing the ability to distill complex CS concepts for students and engineers alike. Notably, he pairs production-grade engineering with a track record of creating reproducible tooling and analytic algorithms across domains, from retinal video analysis to mobile app test infrastructure.
9 years of coding experience
7 years of employment as a software developer
B.S. Electrical Engineering & Computer Sciences Minor in Bioengineering, B.S. Electrical Engineering & Computer Sciences Minor in Bioengineering at University of California, BerkeleyMSc Machine Learning and Data Science, MSc Machine Learning and Data Science at Imperial College London
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (17)
scanning10
computer-vision9
nwb8
python8
open-data8
hdf8
reproducible-research8
open-science7
neuroscience7
quantile5
data-science4
jekyll4
open-source4
beam3
data-format3
Programming languages (5)
Github contributions (5)
UTEP-CS-1/website
Aug 2023 - Dec 2023
Contributions:225 pushes, 1 branch in 3 months
lowvisionresearch/ReVAS
Mar 2017 - Sep 2019
Retinal Video Analysis Suite: a utility for generating reference frames and extracting eye position traces from retinal videos recorded via scanning laser ophthalmoscopes. It also includes a set of tools for classifying eye movements into drifts and (micro)saccades, and extracting detailed information about these eye movements.
Contributions:367 commits, 6 PRs, 71 pushes in 2 years 6 months
positiontracespythonlaservideo-analysis
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