Machine Learning System Design Interview, issue 1, Nov 24, 2025

The Auto-Bidder Paradox

Quant/ML Engineer interview, and the interviewer asks:

We trained a Transformer-based regression model on 10 million home sales. It achieves an RMSE of 1.5%, significantly beating our human appraisers. We want to auto-bid on \$500M of inventory next month.

Don’t say: Yes, deploy it immediately. The model is superhuman. To be safe, we just apply a Confidence Threshold. We only bid when the model’s uncertainty variance is low (e.g., \< 2%). We can also cap the maximum bid at 5% below the predicted market value to bake in a margin of safety.

Why symmetric loss turns your model into a toxic-asset machine - and how quantile-based losses fix it.

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