Ian Flint
Senior Director Of Engineering at NVIDIA
Cupertino, California, United States
Summary
👤
Senior🎓
Top SchoolIan Flint is a senior engineering leader with 11 years in hyperscale infrastructure and a 25+ year tech career spanning startups to hyperscale operators, now leading engineering at NVIDIA from Cupertino. He has overseen massive network capacity (100+ Tbps at Verizon Media/Yahoo) and driven network automation, monitoring, and service architecture across large distributed systems. A hands-on principal, he has contributed to open-source tooling for time-series anomaly detection (egads) by improving usability, serialization, and logging—demonstrating both operational experience and backend engineering chops. His background includes founding and building payment and operations platforms (Billpoint, Bix) and delivering durable systems still in production years later, reflecting a focus on long-lived, maintainable infrastructure. Fluent in bridging product, ops, and architecture, he combines naval discipline with entrepreneurial instincts to scale complex systems reliably.
10 years of coding experience
24 years of employment as a software developer
B.A. Computer Science, B.A. Computer Science at Rice UniversityNorth Shore Country Day School NSCDS
English
Stackoverflow
1reputation
485reached
0answers
1question
Github Skills (11)
javas10
java10
serialization9
data-serialization9
time-series9
big-data9
logging7
refactoring7
bot-framework6
nodejs6
amazon-web-services6
Programming languages (2)
Github contributions (4)
yahoo/egads
Jun 2015 - Jun 2015
A Java package to automatically detect anomalies in large scale time-series data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 2 PRs, 1 comment in 3 days
Contributions summary:Ian made several contributions focused on improving the usability and functionality of the anomaly detection package. These contributions included enhancing the input format, fixing bugs, and modifying the output format for better data analysis. The user refactored code to remove dependencies and moved anomaly detection logging to log4j, indicating improvements to code structure and monitoring. Further work involved converting models to interfaces and introducing serialization capabilities for enhanced data handling.
outlier-detectionanomaly-detection-modelsscalebig-dataseries-data
ianflint/egads
Jun 2015 - Jun 2015
Contributions:15 commits, 2 PRs, 19 pushes in 5 days
anomalyanomaly-detectionmachine-learninggeneric
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