Choose a project where you owned a meaningful ML component end-to-end, and structure your answer around the problem, your specific technical decisions, and the measurable business impact. Be explicit about trade-offs you weighed and alternatives you rejected, since DoorDash interviewers will probe depth over breadth. Keep the initial walkthrough to 3-4 minutes, then let follow-up questions drive the technical detail.
Pro tip: Anchor every technical decision to a business metric (e.g., delivery time, conversion, or dasher utilization) and proactively name one thing that went wrong and how you fixed it — volunteering failure signals seniority and preempts the interviewer's follow-up.
Briefly describe the team, the business problem, and why it mattered (e.g., improving ETA accuracy or store ranking). State the success metric up front so the interviewer knows what 'good' looks like.
Clearly delineate what you personally built versus what the team did. Use 'I' for your work and 'we' for collaboration, and name the specific models, pipelines, or systems you owned.
Walk through 1-2 key decisions (e.g., model choice, feature engineering, latency vs. accuracy) and the alternatives you considered. Justify why your choice won given the constraints.
Describe who you worked with (PM, data engineering, ops) and one concrete thing that went wrong — a data drift issue, a failed experiment, a misalignment — and how you resolved it.
End with measurable results (e.g., 'reduced ETA error by 12%' or 'increased conversion by 3%') and a brief lesson learned or what you'd do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.