Aaron Loo
Staff Security Software Engineer at Aurora
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAaron Loo is a Staff Security Software Engineer in San Francisco with ten years of experience building measurable, pragmatic security systems—currently hardening autonomous vehicles at Aurora with ZTA, hardware-backed device attestation, and process-level PKI hierarchies. He previously led security engineering at Airbnb and Yelp, where he built a company-wide quantitative risk engine and scaled application security through both hands-on engineering and people leadership. An active open-source contributor to projects like Yelp/detect-secrets, he improves secret-detection tooling, benchmarking, and multi-repo scanning. A former battalion signal officer (once the youngest lieutenant in his unit) and dual-degree graduate in Computer Science and Business from the University of Michigan, he brings operational discipline and a “professionally paranoid” mindset to complex, safety-critical systems.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Engineering (BE), Computer Science, Bachelor of Engineering (BE), Computer Science at University of MichiganBachelor of Business Administration (BBA), Business Administration and Management, General, Bachelor of Business Administration (BBA), Business Administration and Management, General at University of Michigan - Stephen M. Ross School of Business
English
Stackoverflow
1reputation
149reached
0answers
1question
Github Skills (8)
git10
python10
security10
testing9
benchmarking9
benchmark9
user-controls6
designmode6
Programming languages (8)
Github contributions (5)
Yelp/detect-secrets
Feb 2018 - Apr 2021
An enterprise friendly way of detecting and preventing secrets in code.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 128 reviews, 125 commits in 3 years 2 months
Contributions summary:Aaron focused on refactoring and enhancing the core functionality of the `detect-secrets` repository. Their contributions included restructuring code snippets, handling Unicode strings, and improving benchmark scripts by adding JSON output, the ability to choose plugins, and timeout functionality. Further improvements involved adding baseline functionality and performance tests to the benchmark script. The user also added features to support scanning multiple git repositories and implemented changes related to verifiable secrets.
secrets-detectionsecuritysecretsyelpdetecting
domanchi/gibberish-detector
Nov 2020 - Feb 2022
Train a model, and detect gibberish strings with it.
Contributions:1 review, 21 commits, 3 PRs in 1 year 3 months
stringsmachine-learningdetectgibberishtrain
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