How does a Vision Transformer (ViT) work, and when does it beat a CNN?
Patches as tokens, global attention from layer one, and a weaker inductive bias than a CNN. What interviewers reward is naming the data regime where each architecture wins and why. Here is the answer interviewers score highest.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Patches as tokens, global attention from layer one, and a weaker inductive bias than a CNN. What interviewers reward is naming the data regime where each architecture wins and why. Here is the answer interviewers score highest.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
