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:
What is the focus of your work these days?
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.
And what was the motivation behind your talk?
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.
Speaker
Sudeep Das
Head of Machine Learning and Artificial Intelligence, New Business Verticals @DoorDash, Previously Machine Learning Lead @Netflix, 15+ Years in Machine Learning
Sudeep Das is the Head of Machine Learning and Artificial Intelligence, New Business Verticals, at DoorDash, leading Personalization, Search, and Product Catalog, and Fulfillment & Inventory related ML applications within the New Verticals. He was previously a Machine Learning Lead at Netflix, where his main focus was on developing the next generation of machine learning algorithms to drive the personalization, discovery and search experience in the product. Sudeep has had more than fifteen years of experience in machine learning applied to both large scale scientific problems, as well as in the industry. He is a frequent speaker at RecSys, SIGIR, ICML, ReWork, MLConf, Nordic Media Conference, and other machine learning conferences. He holds a PhD in Astrophysics from Princeton University.
Speaker
Pradeep Muthukrishnan
Head of Growth for New Business Verticals @DoorDash, Previously Founder & CEO @TrustedFor, 15+ Years in Machine Learning
Pradeep is the Head of Growth for New Business Verticals at DoorDash, where he leads end-to-end growth efforts across personalization, targeting, lifecycle marketing, off-platform acquisition, and new product experiences. His work spans emerging categories like grocery, convenience, alcohol and retail. Before DoorDash, Pradeep was the founder and CEO of TrustedFor, a Y Combinator-backed startup that reimagined professional networking through high-fidelity recommendations. A serial builder, he has bootstrapped successful recruiting ventures and launched multiple 0-to-1 products at early-stage startups. Pradeep holds a PhD in Machine Learning from the University of Michigan and has spent the last 15 years applying cutting-edge ML research to drive growth across consumer marketplaces.
