AI Engineering that Delivers: Blueprint to Impact

This engineering-focused track showcases battle-tested architectural decisions and implementation frameworks that transform theoretical GenAI concepts into production systems, equipping technical leaders with concrete approaches for building LLM infrastructure and applications that scale reliably and deliver measurable business impact

From this track

Session

From Reinforcement Learning Enhanced Image to AI Collection Generation: How Pinterest Cracked the Code on Content Discovery

his talk presents Pinterest's journey in deploying AI at massive scale, from using Reinforcement Learning to create images to building 

Speaker image - Faye Zhang

Faye Zhang

Staff Software Engineer @Pinterest, Tech Lead on GenAI Search Traffic Projects, Speaker, Expert in AI/ML with a Strong Background in Large Distributed System

Session

Automating the Web With MCP: Infra That Doesn’t Break

AI agents are only as strong as the infrastructure beneath them. In this talk, we’ll walk through the architecture behind Browserbase’s model context protocol (MCP), built to support stateful browser automation at scale.

Speaker image - Paul Klein

Paul Klein

Founder @Browserbase, previously Director of Self-Service & Engineering Manager @Mux, Co-Founder & CTO @Stream Club, Technical Lead @Twilio Inc.

Session

Dynamic Moments: Weaving LLMs into Deep Personalization at DoorDash

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.

Speaker image - Sudeep Das

Sudeep Das

Head of Machine Learning and Artificial Intelligence, New Business Verticals @DoorDash, Previously Machine Learning Lead @Netflix, 15+ Years in Machine Learning

Speaker image - Pradeep Muthukrishnan

Pradeep Muthukrishnan

Head of Growth for New Business Verticals @DoorDash, Previously Founder & CEO @TrustedFor, 15+ Years in Machine Learning

Track Host

Faye Zhang

Staff Software Engineer @Pinterest, Tech Lead on GenAI Search Traffic Projects, Speaker, Expert in AI/ML with a Strong Background in Large Distributed System

Faye is a Staff Software Engineer at Pinterest, where she leads AI-driven search traffic initiatives and launched the company's first successful GenAI production experiment, driving significant user engagement growth. With a Computer Science degree from Georgia Tech and ongoing AI graduate studies at Stanford, she combines deep technical expertise in distributed systems with cutting-edge AI research. Her work spans both industry and academia, including contributions to university genomic science research. She regularly shares insights on AI innovation at technical conferences in San Francisco and Paris, focusing on scalable AI solutions that transform user experiences.

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