DoorDash 2025 Summer Intern Projects: GenAI Voice Agent, Storm Mode ETA, Probabilistic ETA Model, and LLM Alcohol Recommendations
DoorDash's ETA model was thrown off during high-stress events while live operators relied on unvalidated, untracked CSV files; the robocall system could only capture yes/no responses on the day of a closure rather than capturing detailed hours in advance; the Weibull-based ETA model's assumptions may not reflect true delivery time distributions; and the Alcohol Landing Page lacked personalized item carousels, limiting product discovery.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Voice call provider selection
The scheduler picks the voice call provider via factory based on store configuration and feature flags.
Tools used
VapiKafkaLarge Language ModelsEntityCache
Outcome
DoorDash shipped a structured Logistics Console with storm mode automation and an ETA pad auditor, a GenAI voice agent that captures natural language merchant responses to update store hours, two probabilistic ETA model candidates that achieved statistically significant improvement in on-time arrival in an A/B test, and an LLM-powered alcohol recommendation carousel with generated titles.
What failed first
The existing bulk CSV tool offered no historical evidence and forced the ETA team to query multiple databases for change history; the DTMF robocall system was limited to simple button-press responses and could not capture detailed hours in advance.
Results
Time savedstatistically significant improvement in OTA and earliness