DoorDash-Specific ML Applications Questions
Domain-specific machine learning use cases within the DoorDash platform, covering production ML lifecycle topics such as demand forecasting, driver dispatch and routing, pricing and revenue optimization, recommendations, fraud detection, and real-time optimization. Includes model development, deployment, monitoring, drift handling, and scalability considerations for ML systems in a high-velocity delivery marketplace.
HardTechnical
35 practiced
Propose methods to quantify and propagate uncertainty in demand forecasts (e.g., quantile regression forests, Bayesian models, ensembles) and explain how downstream systems such as dispatch and staffing should use prediction intervals rather than point estimates. Address calibration and how to measure interval quality.
MediumTechnical
32 practiced
Write a Python function that aggregates order events into 500m grid cells per hour. Input: list of events with fields (order_id, lat, lon, event_ts ISO string). Output: mapping from grid_id to hourly counts for the last 24 hours. Describe your grid indexing approach (e.g., H3 or fixed lat/lon buckets) and ensure the solution is efficient for about 1M events.
HardTechnical
33 practiced
Design an embedding-based recommendation architecture to serve millions of users and restaurants on DoorDash. Cover embedding training strategies (co-occurrence, matrix factorization, or deep learning), online retrieval with approximate nearest neighbor indices, memory and latency trade-offs, embedding freshness, and approaches to handle cold-start items and users.
HardTechnical
34 practiced
Describe the end-to-end process to validate and certify a fraud detection model for internal audit and regulatory compliance at DoorDash. Cover data lineage, model versioning, reproducibility, threshold selection, human-in-the-loop review policies, logging for audit trails, and how to prepare evidence for auditors.
EasyTechnical
39 practiced
Explain the key metrics you would use to evaluate demand forecasting models at DoorDash for predicting orders per hour per zone. Compare MAE, MAPE, RMSE and discuss which metric(s) you would prioritize given heavy-tailed demand, many zero-count intervals, and business needs such as staffing and incentives. Mention how outliers and intermittent demand affect metric choice.
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