What is self-supervised learning, and how do contrastive methods and masked prediction work?
Self-supervision is how modern models pretrain on unlabeled data, the engine behind LLMs and modern vision. The signal is the pretext-task idea and the genuine difference between contrastive and masked-prediction objectives.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Self-supervision is how modern models pretrain on unlabeled data, the engine behind LLMs and modern vision. The signal is the pretext-task idea and the genuine difference between contrastive and masked-prediction objectives.
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.
