Senior Causal ML Engineer - Production Systems

DoorDash; Sunnyvale (CA); On-site; USD 190,000 - 280,000; Full time

Benefits offered by this job

Equity grants

401(k) with employer matching

16 weeks paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical, dental, vision benefits

11 paid holidays

Disability insurance

Mental health program

Job summary

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. This is not a generic ML role with some experimentation work on the side.

We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.

Qualifications

• Deep practical experience with causal inference or causal ML.
• Experience shipping models in production for consumer marketplaces or ads.
• Strong judgment on experiments vs observational estimation.
• Comfort with methods such as IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.
• Strong ML engineering ability and cross-functional collaboration.

Responsibilities

• Design and productionize causal ML systems that influence marketplace decisions across New Verticals.
• Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and interventions.
• Connect experimentation data and ML decisioning to enable faster, more reliable tradeoffs.
• Translate causal models into production systems affecting ranking, targeting, and growth.
• Collaborate with economists, data scientists, and product teams to apply robust methods.

Skills

Causal inference

Econometrics

Experimentation

Production ML

ML engineering

Decision systems

A/B testing

Off-policy evaluation

Job description

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. This is not a generic ML role with some experimentation work on the side.

We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.