below marketmost similar roles pay hereabove market
This role pays more than
65%
of similar roles. Most pay
$211,200–$254,750
— the blue band above.
At the midpoint, this role pays about
$251k
versus about
$233k
for comparable roles.
Based on 240 similar postings.
Employer
About DoorDash, Inc
DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.
DoorDash, Inc currently has
97 open roles
on FindRole.
Listed pay typically runs
$163,800–$240,900
across 96 roles with salary data.
The Staff Machine Learning Engineer, Causal Inference joins a senior pod of causal ML and econometrics experts to build the causal foundation for New Verticals, including grocery, retail, and alcohol. You will design and productionize causal ML systems that influence marketplace decisions by building uplift and heterogeneous treatment effect models for consumer lifecycle value, promotions, and retention. Your daily work involves developing counterfactual evaluation frameworks for ranking, search, and marketplace interventions while connecting experimentation, observational data, and ML decisioning. You will utilize advanced methods including doubly robust estimation, double ML, IV, diff-in-diff, CUPED, contextual bandits, and off-policy evaluation. This role solves the technical challenge of making high-dimensional marketplace decisions where randomized experiments are slow or incomplete, ensuring long-term marketplace health through rigorous causal reasoning.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 companies.
Design, build, and productionize causal ML systems to influence marketplace decisions across New Verticals.
Build uplift and heterogeneous treatment effect models for consumer lifecycle value, promotions, and retention.
Develop counterfactual evaluation frameworks for ranking, recommendations, search, and marketplace interventions.
Create systems connecting experimentation, observational data, and ML decisioning to enable better trade-offs.
Design surrogate metrics and early indicators to accelerate team movement while preserving marketplace health.
Translate causal models into production systems for ranking, targeting, budget allocation, and inventory-aware discovery.
Partner with econometrics leaders to apply methods like doubly robust estimation, IV, and double ML.
Establish standards for causal reasoning, model trust, and debugging causal claims across ML teams.
What we're looking for
Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
Experience shipping models or decision systems in production, ideally in high-scale settings like marketplaces, ads, or logistics.
Strong ML engineering ability to build reliable pipelines, train models, and evaluate them rigorously.
Proficiency in methods including doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.
Ability to design and productionize causal ML systems for marketplace decisions like ranking, promotions, and search.
Strong product judgment to connect causal methods to business decisions rather than just offline metrics.
Ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.