Machine Learning Engineer - Conversation AI

Doordash - Sunnyvale, CA

Full-time - Mid Level

Sunnyvale, CA

Couriers and Messengers

About the position

As a Machine Learning Engineer at DoorDash, you will play a pivotal role in enhancing our conversation AI and personalization ecosystem. Your primary responsibility will be to identify and prioritize machine learning investments that can significantly impact our three-sided marketplace, which includes consumers, merchants, and dashers. You will leverage our robust data infrastructure to develop advanced natural language processing (NLP) and personalization models that will enhance the user experience for millions of customers. Collaborating closely with an engineering lead and product manager, you will set strategic directions that align with our business metrics and growth objectives. In this role, you will lead the development of DoorDash's support chatbot and large language model (LLM) system. This includes implementing techniques such as active learning, semi-supervised learning, weak label generation, and data augmentation strategies to improve the support experience for consumers, dashers, and merchants. You will also drive the personalization of our issue prediction and resolution policies, utilizing recommendation systems and dynamic pricing modeling technologies to provide tailored solutions for our users. Additionally, you will spearhead the creation of next-generation LLM AI Agent tools, including a Co-pilot system that will transform how users interact with our support system. You will employ advanced experiment design techniques, such as stratification and variance reduction, to create A/B tests that efficiently measure the impact of your innovations while minimizing risks to the broader system. This role offers a unique opportunity to make a significant impact on the way DoorDash operates and serves its users.

Responsibilities

• Lead the development of DoorDash's support chatbot and LLM system.
• Implement active learning, semi-supervised learning, and weak label generation techniques.
• Drive the personalization of issue prediction and resolution policies.
• Utilize recommendation systems and dynamic pricing modeling technologies.
• Spearhead the creation of next-generation LLM AI Agent tools.
• Build a Co-pilot system to enhance user interaction with support systems.
• Employ advanced experiment design techniques for A/B testing.
• Measure the impact of innovations while minimizing risks to the broader system.

Requirements

• 3+ years of industry experience developing optimization models with business impact.
• 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.
• Deep understanding of natural language processing techniques.
• Experience in big data analysis and system integration with ML solutions.
• Good understanding of statistics, machine learning, operations research, and causal inference.
• Familiarity with programming languages such as Python and machine learning libraries like SciKit Learn and Spark MLLib.
• Experience in productionizing and A/B testing machine learning models.

Nice-to-haves

• Familiarity with advanced causal inference techniques.
• Experience with contextual bandit algorithms.

Benefits

• Comprehensive healthcare benefits.
• 401(k) plan with employer match.
• Short-term and long-term disability coverage.
• Basic life insurance.
• Wellbeing benefits.
• Paid time off and paid parental leave.
• Several paid holidays.
• Opportunities for equity grants.