Your GPUs sit at 40% utilization during training. How do you find and fix the bottleneck?
Paying for accelerators that sit idle half the time is the most common waste in ML training, and the reflex to add more GPUs only makes it worse. The interviewer wants the profiling discipline that pinpoints what is starving them.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Paying for accelerators that sit idle half the time is the most common waste in ML training, and the reflex to add more GPUs only makes it worse. The interviewer wants the profiling discipline that pinpoints what is starving them.
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.
