Machine Learning System Design Interview #44 - The Invariance Illusion

Why throwing random noise at your training loop quietly masks critical edge-case failures and how to build deterministic metamorphic gates that catch semantic vulnerabilities before runtime.

You’re in a Senior Computer Vision Engineer interview at Meta. The interviewer sets a trap:

“Your medical imaging model shows a flawless 0.99 AUC offline, but plummets to 0.65 when deployed because real-world clinic scans undergo minor 3-degree rotations and arbitrary cropping. How do you redesign your CI/CD evaluation pipeline to catch these semantic v…