Senior Causal ML Engineer for Production Systems

DoorDash; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Benefits offered by this job

Equity grants

401(k) with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Sick leave

Job summary

DoorDash seeks a Causal Machine Learning Engineer to build the causal ML backbone behind growth across New Verticals. You will design and productionize uplift and heterogeneous treatment effect models, connect experimentation with observational data, and develop evaluation frameworks for ranking, recommendations, and promotions.

You will work with ML engineers, economists, data scientists, product managers, and business leaders to translate causal insights into scalable decisions that impact

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping production ML models or decision systems in large consumer marketplaces.
• Strong judgment on tradeoffs between randomized experiments and observational estimations.
• Comfort debating uplift methods and off-policy evaluation in production environments.
• Able to build reliable pipelines and partner with platform teams for production deployment.
• Capable of connecting methods to business decisions beyond offline metrics.

Responsibilities

• Design, build, and productionize causal ML systems that influence marketplace decisions across New Verticals.
• Develop uplift/heterogeneous treatment effect models for lifecycle value, promotions, and retention.
• Create counterfactual evaluation frameworks for ranking, recommendations, search, and interventions.
• Bridge experimentation, observational data, and ML decisioning to improve tradeoffs.
• Design surrogate metrics and early indicators to accelerate development without compromising health.
• Collaborate with econometrics and analytics leaders to choose robust methods.
• Translate causal models into production systems shaping ranking, budget, and growth.
• Raise the bar for causal reasoning and debugging in real marketplaces.

Skills

Causal inference

Production ML systems

Econometrics

Experimentation platforms

Uplift modeling

Contextual bandits

Off-policy evaluation

ML engineering

Product judgment

Cross-functional collaboration

Job description

DoorDash seeks a Causal Machine Learning Engineer to build the causal ML backbone behind growth across New Verticals. You will design and productionize uplift and heterogeneous treatment effect models, connect experimentation with observational data, and develop evaluation frameworks for ranking, recommendations, and promotions.

You will work with ML engineers, economists, data scientists, product managers, and business leaders to translate causal insights into scalable decisions that impact