Joshua Morton
Staff Site Reliability Engineer at Google
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJoshua Morton is a Staff Site Reliability Engineer in San Francisco with 12 years of experience building and scaling reliable systems at Google. He combines strong theoretical foundations from Georgia Tech (Automated Intelligence and Theory) with practical expertise in analytics, algorithms, Python, and software design to optimize complex infrastructure. His progression from intern to staff engineer reflects a track record of shipping production tooling and improving service performance and operability. An active contributor to open-source algorithmic tooling, he notably optimized core matrix operations in the ABAGAIL library, demonstrating an eye for performance engineering at the numerical level. Colleagues describe him as a pragmatic problem-solver who balances deep technical rigor with measurable operational impact.
12 years of coding experience
6 years of employment as a software developer
Computer Science, Computer Science, Computer Science, Computer Science at Georgia Institute of Technology
English, Spanish
Github Skills (10)
javas10
machine-learning10
mat10
matrix10
java10
optimization9
optimizers9
optimisation9
algorithm9
unit-testing9
Programming languages (6)
Github contributions (5)
pushkar/ABAGAIL
Feb 2015 - Mar 2015
The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 commits, 1 PR in 12 days
Contributions summary:Joshua primarily contributed to the `FastMatrix` class, implementing and improving matrix operations within the project. Their work included adding transpose, getRow, and getColumn functionality. They also focused on optimizing matrix multiplication, resulting in significant performance improvements. Furthermore, they added testing to compare matrix speeds of various implementations.
optimization-algorithmsartificialmachine-learningneural-networkartificial-intelligence-algorithms
joshuamorton/tidbits
Nov 2013 - Mar 2016
Contributions:19 commits, 5 pushes, 6 issues in 2 years 4 months
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.