Christos Bampis
Senior Software Engineer at Netflix
Los Gatos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChristos Bampis is a senior software engineer at Netflix with a decade of experience specializing in video quality and encoding for streaming. He holds a PhD from UT Austin focused on perceptual video quality and Quality of Experience, and his research informs practical improvements in Netflix’s Video Algorithms team. At Netflix he contributes to production-grade systems and to open-source tooling like the well-known VMAF project, where he strengthened test automation and robustness for perceptual quality metrics. His background spans academic research, industry R&D internships, and hands-on engineering, bridging rigorous evaluation methods with scalable deployment. Based in Los Gatos, he brings both deep signal-processing expertise and a pragmatic focus on reliability and performance at scale. An understated strength is his habit of translating complex perceptual models into maintainable test frameworks that improve trust in quality metrics.
10 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at The University of Texas at AustinM.Eng. and Diploma, Electrical and Computer Engineering, M.Eng. and Diploma, Electrical and Computer Engineering at National Technical University of Athens
English, French, German
Github Skills (5)
unit-testing10
test-framework10
python10
test-automation10
testing10
Programming languages (2)
Github contributions (5)
Netflix/vmaf
Jun 2018 - Dec 2021
Perceptual video quality assessment based on multi-method fusion.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:1 release, 6 reviews, 57 commits in 3 years 6 months
Contributions summary:Christos primarily contributed to the testing framework of the VMAF project, refactoring unit tests to reduce code redundancy. Their work included modifying test files and adding utilities to run tests, specifically focusing on the VMAF quality assessment metrics. The user's changes touched multiple test files across different quality runners, indicating a focus on ensuring the robustness and accuracy of the VMAF evaluation process. The commits included both functional and performance test cases.
pytorchvideo-quality-assessmentfeature-detectionperceptualmethod
christosbampis/vmaf
Aug 2017 - Jul 2023
Perceptual video quality assessment based on multi-method fusion.
Contributions:2 PRs, 83 pushes, 54 branches in 5 years 11 months
pytorchvideo-quality-assessmentperceptualmethodmachine-learning
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