James Cline
Staff DevOps Engineer at Alpaca
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJames Cline is a Staff DevOps Engineer with 16 years of experience building high-performance, secure, and scalable systems across CDN, fintech, and security startups in the San Francisco Bay Area. He specializes in TLS, certificate deployment, and infrastructure optimizations—having led TLS performance and certificate microservice work that achieved multi-order-of-magnitude propagation improvements at Edgecast. Comfortable across systems and languages, he has a history of refactoring core libraries for performance and maintainability (notably contributing algorithm and STL refactors to the mlpack C++ library). A pragmatic engineer and mentor, James blends deep infosec knowledge with low-level performance tuning to reduce latency, memory use, and IO overhead in production systems. He spends free time exploring programming languages, cryptography, and concurrency, often learning by writing real software rather than just reading about it.
16 years of coding experience
14 years of employment as a software developer
Bachelor of Science (B.Sc.), Computer Science, Bachelor of Science (B.Sc.), Computer Science at Georgia Institute of TechnologyA.S., Computer Science, A.S., Computer Science at Central Alabama Community College
Japanese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
algorithm10
data-structures10
algorithms10
stl10
c-language10
cprogramming-language10
data-structure10
implement10
k-means-clustering9
Programming languages (6)
Github contributions (5)
mlpack/mlpack
May 2010 - Apr 2012
mlpack: a fast, header-only C++ machine learning library
Role in this project:
Back-end Developer & Algorithm Implementer
Back-end Developer & Algorithm ImplementerContributions:118 commits in 1 year 11 months
Contributions summary:James's primary focus was on refactoring the mlpack library to utilize `std::vector` and other STL components, replacing custom data structures like `ArrayList` and other custom implementations. They modified core data structures, such as `RangeSet` and `MinHeap` and implemented the conversion to use `std::vector`, along with changes to `Queue`. Furthermore, they made improvements to the k-means algorithms. This indicates a focus on improving the performance and maintainability of the core components.
cppmachine-learningmlpackmachine-learning-libraryc-plus-plus
jcline/goto
Feb 2014 - Jan 2017
Contributions:26 commits, 4 pushes in 2 years 11 months
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