Staff Machine Learning Engineer, Causal Inference at DoorDash USA
About the role
About the Team DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation pla…
Interview process at DoorDash USA
General interview process (this company's specific loop isn't published - reach out if you have insider info).
- Recruiter phone screen - typically 20-40 min
- Technical screen - 1 coding problem or take-home; format varies by company
- Onsite / virtual loop - typically 4-6 rounds over 2-3 weeks; exact split of coding, systems design, and behavioral varies by role and location
Platforms used: unknown — MiPrep tested compatible with major platforms
How to prepare
MiPrep helps you rehearse the specific cadence and question types DoorDash USA interviewers score on. Upload your resume and this job description; MiPrep generates a rehearsed answer set in your voice from your own projects.