scale

A million rows does not automatically mean deep learning. The signal is reasoning from dimensionality, data type, inference latency, and label budget, then noting where KNN quietly survives as approximate nearest neighbor. Here is the answer.

The canonical AI system-design question. The signal is covering both the model lifecycle and the serving stack under real scale, without rambling. Most candidates design only half and lose the points.

Real-time transcription at scale tests streaming ASR, latency budgets, and GPU fleet management under heavy concurrency. The signal is streaming chunked inference with partial results, not batch transcription. Here is the design.

Two models chained (ASR then summarization), a long-context problem hiding inside, and a faithfulness bar where a fabricated action item has real consequences. The signal is the pipeline plus how you keep a 3-hour transcript honest at thousands a day.

Every team reinvents a spreadsheet of training runs and then drowns in it. The interviewer wants the platform that ingests runs, params, metrics, and artifacts at high write volume, links them by lineage, and makes thousands of experiments comparable, which is a different system from a model registry.