Engineering Manager, Personalization

DoorDash•San Francisco, CA

16d

About The Position

DoorDash is seeking an Engineering Manager with a machine learning background to lead the Personalization engineering team within the Core Consumer organization. The team builds machine learning ranking and recommendation systems that maximize value for consumers, merchants, and DoorDash, and supports the consumer experience from the initial landing page through checkout across platforms. As DoorDash expands into new verticals and experiences, the team is reimagining personalization through consumer-intent prediction, semantic understanding of ranked entities, content blending across verticals and business lines, and real-time ranking that adapts to consumer signals. The role leads a team of machine learning engineers and helps define DoorDash’s personalization strategy and technology roadmap. The manager will deliver end-to-end personalization experiences using state-of-the-art machine learning, partner with Product, Design, and Business stakeholders, foster innovation and high engineering standards, and grow the team through coaching, delegation, internal development, and external recruiting.

Requirements

B.S., M.S., Ph.D. in Computer Science, or equivalent.
At least 7 years of industry experience.
At least 2 years of leadership experience.
Broad knowledge of machine learning and a strong ML modeling foundation.
Extensive experience building user-facing products and working directly with product managers.
Strong communication skills and ability to partner with teams across disciplines.
Ability to guide and grow an excellent engineering team in a rapidly changing business environment.

Nice To Haves

Experience leading successful applications of machine learning to real-world ranking and recommendation problems.
Experience with LLM-powered agents, semantic representations of users and content, multi-objective blending, or real-time ML systems.

Responsibilities

Build an intent agent that predicts what each consumer is looking for when they open the app and shapes the experience accordingly.
Use LLMs and deep learning approaches, including GNNs, multi-task learning, and transformers, to improve personalization, beginning with semantic understanding of ranked entities.
Extend the content blender to combine content from different verticals and business lines, including reservations, going out, and ads, while balancing value for consumers, merchants, and DoorDash.
Advance personalization toward real-time ranking that responds to consumer signals as they happen.
Lead and grow a team of machine learning engineers delivering end-to-end personalization experiences with state-of-the-art ML approaches.
Partner with Product, Design, and Business stakeholders to define the team’s roadmap and vision and deliver impact.
Encourage innovation, adoption of cutting-edge technologies, outside-the-box thinking, and teamwork.
Coach and empower engineers through delegation while applying technical expertise to maintain high engineering standards.
Scale the team by developing internal talent and attracting external talent.