Matthew Purland
AI ML (Stealth Mode)
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Purland is a senior software engineer with 16 years of production experience who recently transitioned into applied AI/ML, building end-to-end PyTorch training pipelines, LLM/SLM fine-tuning, RAG systems, agentic workflows, MCP, and reinforcement learning policy engineering. He combines deep expertise in mobile and platform engineering—leading iOS/Android architecture, performance tuning, backend integrations, and developer tooling—with rigorous software engineering practices like CI/CD, observability, and testable codebases. As a tech lead and mentor he has guided cross-functional projects from prototype to production at startups and scale-ups, including founding engineering roles and multiple acquisitions. An active open-source contributor, he improved JSON parsing and generic collection ergonomics in the Swiftz functional programming library, demonstrating attention to API ergonomics and robust testing. Based in San Francisco, he brings pragmatic engineering judgment, a track record of shipping reliable systems, and a knack for translating research techniques into production ML systems.
16 years of coding experience
17 years of employment as a software developer
Computer Science Mathematics, Computer Science Mathematics at University of Minnesota
Stackoverflow
290reputation
29kreached
9answers
0questions
Github Skills (13)
unit-testing10
swift10
functional-programming10
json-serialization10
data-structure9
data-structures9
gdb6
drawimage6
bfd6
ios6
uiwebview6
iphone6
facebook6
Programming languages (5)
Github contributions (5)
typelift/Swiftz
Jul 2014 - Jan 2016
Functional programming in Swift
Role in this project:
Back-end Developer
Back-end DeveloperContributions:32 commits, 23 PRs, 12 pushes in 1 year 5 months
Contributions summary:Matthew primarily focused on improving JSON serialization and deserialization within the Swiftz library. They enhanced the library's ability to handle various numeric types, including NSNumber, Int, Int64, UInt64, Bool, Double, and Float, adding comprehensive unit tests to validate the changes. Furthermore, they refactored the Array and Dictionary generic syntax to use literals, increasing code readability. Additionally, the user added features for optional parameter handling in JSON parsing and support for enum types.
lensesswiftfunctional-programming
mpurland/SwiftMD5
Mar 2016 - Mar 2017
A pure Swift implementation of MD5
Contributions:2 releases, 11 commits, 10 pushes in 11 months
md5swiftpure-swift
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