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Your GPUs sit at 40% utilization during training. How do you find and fix the bottleneck?

Paying for accelerators that idle half the time is the most common waste in ML training, and the instinct to add more GPUs makes it strictly worse. The interviewer wants the profiling discipline that finds what is starving them.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Paying for accelerators that idle half the time is the most common waste in ML training, and the instinct to add more GPUs makes it strictly worse. The interviewer wants the profiling discipline that finds what is starving them.

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