Speaker
Abstract
In this talk, we’ll walk through how DoorDash is redefining personalization by tightly integrating cutting-edge large language models (LLMs) with deep learning architectures such as Two-Tower Embeddings (TTE) and Multi-Task Multi-Label (MTML) models.
We'll begin by sharing how delivering truly dynamic, moment-aware experiences first requires a robust, structured understanding of our product catalog. We'll detail how we automated the generation of a product knowledge graph using LLMs, enhanced with fine-tuning and agentic workflows.
From there, we'll demonstrate how the synergy between LLMs and DL-powered personalization unlocks new frontiers in dynamic content generation — powering collections, modules, and real-time app assets designed to win users’ “moments” (e.g., movie nights, flu seasons, study snacks). Our framework turns DoorDash into a moment-ready utility that understands and adapts to users’ evolving needs — becoming a one-stop shop for whatever life throws at them.
Key Takeaways:
- A practical framework for integrating LLMs with existing deep learning systems like TTE and MTML for real-time personalization.
- A blueprint for building and maintaining automated product knowledge graphs using fine-tuned LLMs and agentic pipelines.
- How to personalize app experiences dynamically to win high-intent user moments and occasions.
Interview
The majority of our time is spent on designing and scaling systems that combine deep learning-based personalization with generative AI to create adaptive, real-time user experiences. This includes building LLM-powered agents for product enrichment, optimizing MTML/TTE pipelines, and experimenting with end-to-end personalization loops across content, UX, and ranking.
At DoorDash, we’ve seen first-hand how marrying structured predictive models with the generative capabilities of LLMs unlocks entirely new capabilities in product personalization and customer engagement. We believe this is a direction many in the ML community are just beginning to explore, and we want to share our lessons — both technical and organizational — to help others accelerate this convergence. QCon offers the ideal audience for these learnings: technically deep, practically oriented, and curious about real-world AI deployments at scale.
QCon San Francisco 2025 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
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