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
We’re excited about you because you have
- 3+ years of industry experience developing optimization models with business impact, including 1+ year(s) of industry experience serving in a tech lead role
- M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field
- You must be located near one of our engineering hubs which includes : San Francisco, Sunnyvale, Los Angeles, Seattle, and New York
- Deep understanding of natural language processing techniques and procedures for efficiently acquiring and validating human-labeled data
- Good experience in overall big data analysis, system, backed integration with new ML system / solution.
- Good understanding of quantitative disciplines such as statistics, machine learning, operations research, and causal inference
- Familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, Spark MLLib
- Experience productionizing and A / B testing different machine learning models
- Familiarity with advanced causal inferences techniques and contextual bandit algorithms preferred
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