DoorDash AI Engineer Behavioral Interview — Marketplace Impact, Ownership, and Ops-Driven ML

This behavioral interview at DoorDash focuses on how AI Engineers drive measurable impact across the three-sided marketplace (consumers, merchants, dashers) through ownership, data-driven decisions, and fast, pragmatic execution. Interviewers probe for concrete, metric-backed stories that show you can ship production ML/AI systems in a high-velocity, operationally intensive environment. What interviewers look for (based on real candidate experiences): - Ownership and scrappiness: Times you took end-to-end responsibility (problem framing → data/label strategy → model/LLM integration → rollout), unblocked ambiguity, and made trade-offs to deliver customer value quickly. - Data-first decisions: How you defined success metrics (e.g., ETA accuracy, delivery reliability, search/reco CTR, fraud loss rate), designed experiments, interpreted noisy results, and changed course when the data contradicted intuition. - Impact under operational pressure: Handling incidents and regressions during peak periods (dinner rush, promos, holidays), balancing latency/cost vs quality, and partnering with SRE/ops to stabilize systems. - Marketplace intuition and stakeholder empathy: Demonstrating customer obsession across consumers, merchants, and dashers; navigating conflicting incentives; ensuring fairness, safety, and privacy in AI features. - Cross-functional collaboration: Working with PMs, operations, data science, infra, and legal/policy to align on goals, communicate risks, and land decisions; influencing without authority in a 5K–10K person org. - Learning mindset and bar-raising: Postmortems, continuous improvement, mentoring, and raising standards while moving fast. Common prompts/themes you should be ready for: - A time you shipped an AI/ML or LLM-driven feature end-to-end and the exact metrics it moved; why those metrics mattered for DoorDash-like use cases (ETA, dispatch, ranking, search, fraud, support automation). - A difficult prioritization or speed-vs-quality decision; what you cut, what you measured, and how you de-risked rollout. - An incident or negative experiment result; how you diagnosed root cause, communicated with stakeholders, and what changed afterward. - A situation where stakeholders (PM/ops/merchant teams) disagreed on the plan; how you built alignment and earned trust. - How you addressed bias, privacy, or safety concerns in production AI systems affecting merchants or dashers. Interview style and expectations: - Structured behavioral (STAR) with frequent follow-ups for specifics, numbers, and your personal contributions. - Expect deep dives on metrics, experimentation rigor, and operational readiness (alerting, rollback, guardrails). - Clear, concise communication and a bias for action are highly valued; interviewers often ask for what you did next and why within 30–60 second answers. Tip: Bring 4–6 STAR stories that each include the problem context, your decision criteria, measurable outcomes, trade-offs made, and lessons learned. Be ready to quantify impact and discuss how you would adapt the approach to DoorDash’s marketplace.