Nathan Wolfe
Systems Software Engineer - Media Intelligence at Apple
San Diego, California, United States
Summary
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Rockstar🎓
Top SchoolNathan Wolfe is a systems software engineer with a Harvard CS background and 12 years of engineering experience across Google, Cruise, and Apple, currently focused on Media Intelligence Systems and performance. He has deep systems and backend expertise—from YouTube infrastructure to motion-planning developer tools—paired with a strong QA and test-automation mindset honed contributing to well-known open-source trading libraries like Zipline. Nathan’s hands-on work includes fixing subtle numerical edge cases (NaN handling) and strengthening test suites and minute-bar data handling, reflecting an emphasis on correctness and robustness at scale. Based in San Diego, he blends product-facing engineering with developer tooling and systems performance, and consistently brings a data-driven approach to preventing failures before they reach users.
12 years of coding experience
6 years of employment as a software developer
Bachelor of Arts - BA, Computer Science, Bachelor of Arts - BA, Computer Science at Harvard University
Github Skills (12)
unit-testing10
pandas10
pipelining10
python10
algorithmic-trading10
pipe10
pipeline10
testing10
numpy9
data-analysis8
trading4
cryptocurrencies4
Programming languages (1)
Github contributions (5)
quantopian/zipline
Jun 2016 - Sep 2017
Zipline, a Pythonic Algorithmic Trading Library
Role in this project:
Backend Developer & QA Engineer
Backend Developer & QA EngineerContributions:17 commits, 4 PRs, 4 pushes in 1 year 3 months
Contributions summary:Nathan primarily focused on improving the quality and robustness of the Zipline algorithmic trading library. Their contributions involved fixing a bug related to NaN handling in the AverageDollarVolume factor, ensuring accurate calculations. They also added and modified tests to cover NaN cases and partial NaN scenarios, enhancing the reliability of the pipeline. Furthermore, the user introduced a new feature for handling minute bar data in the TradingAlgorithm and refactored the code for panel data handling.
algorithmic-trading-libraryalgorithmic-tradingpythontrading-botbacktesting-trading-strategies
scrtlabs/catalyst
Aug 2016 - Aug 2016
An Algorithmic Trading Library for Crypto-Assets in Python
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:1 commit in 1 day
Contributions summary:Nathan primarily focused on improving the testing infrastructure of the algorithmic trading library. They identified and corrected a bug related to handling NaN values in the `AverageDollarVolume` factor. Their contributions included adding specific test cases to cover NaN scenarios and refining existing tests. Furthermore, the user enhanced the testing framework by adding tests for raw Panel data, ensuring the library's robustness in various data formats.
algorithmic-trading-libraryalgorithmic-tradingpythontrading-botcrypto-assets
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