What is multi-task learning, and when does sharing a model across tasks help or hurt?
One model, multiple objectives, a single shared backbone. Interviewers want you to explain precisely why sharing helps (regularization, data efficiency) and the failure mode that wrecks naive setups. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
One model, multiple objectives, a single shared backbone. Interviewers want you to explain precisely why sharing helps (regularization, data efficiency) and the failure mode that wrecks naive setups. Here is the answer.
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.
