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.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.