How do you choose a loss function (MSE, MAE, Huber, cross-entropy, focal, contrastive)?
The loss sets what the model optimizes, and the wrong choice silently sinks it. What counts is matching the loss to the task and data (outliers, imbalance), rather than reflexively reaching for MSE or cross-entropy.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
The loss sets what the model optimizes, and the wrong choice silently sinks it. What counts is matching the loss to the task and data (outliers, imbalance), rather than reflexively reaching for MSE or cross-entropy.
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.
