Shopify interview questions
Big tech · 2 questions reported
- junior 0
- mid 2
- senior 0
These are 2 technical questions candidates reported being asked in Shopify interviews for AI engineer, ML engineer and applied AI engineer roles, each with a model answer you can say out loud in a minute or two. They cover ml system design: 0 junior, 2 mid-level and 0 senior questions. Every question comes from a public interview write-up that names Shopify.Updated
Shopify mL system design questions
Model answer
Log impressions with position, plus clicks, add-to-carts and purchases, with query and context. Labels: graded relevance, purchase above add-to-cart above click above skip, corrected for position bias, since top slots get clicked regardless. Features: query-product text match, price, ratings, popularity, freshness and user affinity. Model: gradient-boosted trees with a LambdaMART objective, then a neural ranker if it pays. Evaluate with NDCG on later days, then A/B test on conversion and revenue per search.
Model answer
Labels: chargebacks and confirmed fraud are positives, but they arrive weeks later, so train only on orders whose labels have matured. Imbalance: keep every positive, downsample negatives, then re-weight or recalibrate. Features: payment, device, address mismatches and velocities, in gradient-boosted trees. Metrics: precision-recall AUC, not accuracy, and recall at your review capacity. Thresholds come from costs: approve, review or block, set by expected fraud loss against lost good orders.
Each question comes from a public interview write-up or video that names Shopify; the answers are ours. Not affiliated with Shopify.