Machine Learning Engineer - Conversation AI

About the Team

Come help us build the world's most reliable on-demand, logistics engine for delivery! We're looking for an experienced ML Engineer to help us develop and improve the models that power DoorDash's three- sided marketplace of consumers, merchants, and dashers. Team is responsible for building a leading industry level NLP/LLM system to empower all communication for Doordash Journey, also building recommendation/personalization to provide best experience.

About the Role

As a Machine Learning Engineer you will have the opportunity to identify and prioritize machine learning investments across our conversation AI & personalization ecosystem. You will leverage our robust data and infrastructure to develop natural language processing and personalization models that impact millions of users across our three audiences. You will partner with an engineering lead and product manager to set the strategy that moves the business metrics which help us grow our business.

You're excited about this opportunity because you will…

• Lead the development of DoorDash's support chatbot & LLM system: Applying LLM, active learning, semi- supervised learning, weak label generation, documentation embedding/retrieval and data augmentation strategies to improve the consumer, dasher, and merchant support experience

• Drive the personalization of DoorDash's issue prediction & resolution policies: Using both personalization, recommendation and dynamic pricing modeling technologies to serve millions of customers on personalized prediction resolution for any issues they might encounter during their journey

• Spearhead the creation of next-generation LLM AI Agent tools: Building Co-pilot system to evolve how millions of users interact with our support system

• Apply stratification, variance reduction, and other advanced experiment design techniques to create A/B tests to efficiently measure the impact of your innovations while minimizing risk to the broader system

• You can find out more on our ML blog