Pinterest·Machine Learning Engineer·Technical Phone Screen
May 2026
Pinterest ML Engineer interview that centered on a fairly involved evaluation metrics problem, the kind where you're handed multiple confusion matrix tables and asked to compute accuracy, precision, recall, F1, specificity, and sometimes AUC, then pick the best model under a constraint. Not a vibe check round at all.
- You are given multiple tables showing model predictions on labeled datasets (true positives, false positives, true negatives, false negatives). For each table, compute evaluation metrics like accuracy, precision, recall, F1, and specificity, write out the formulas, show your work, and then identify which model or group satisfies a given requirement such as highest precision while keeping recall above a certain threshold.
“This is the kind of question that looks straightforward until you're actually doing it live and second-guessing whether precision is TP/(TP+FP) or the other way around.” The rest of the author's notes on Machine Learning Engineer interview at Pinterest, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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