This role pays more than
59%
of similar roles. Most pay
$211,200–$259,421
— the shaded band above.
At the midpoint, this role pays about
$251k
versus about
$235k
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
187 open roles
on FindRole.
Listed pay typically runs
$144,800–$212,950
across 166 roles with salary data.
Staff Machine Learning Engineer, Causal Inference joins a senior pod of causal ML and econometrics experts to build the causal spine for a large-scale consumer marketplace. This role focuses on developing the causal machine learning foundation for New Verticals, including grocery, retail, and pharmacy categories. The engineer will design and productionize systems such as uplift models, heterogeneous treatment effect models, counterfactual evaluation frameworks, and surrogate metrics to influence decisions in ranking, promotions, and search. Key technical methodologies include doubly robust estimation, double ML, instrumental variables, diff-in-diff, CUPED variance reduction, contextual bandits, and off-policy evaluation. The successful candidate will leverage deep expertise in causal inference and econometrics to translate complex models into production systems that solve marketplace challenges like promotion optimization and inventory-aware discovery while navigating the trade-offs between randomized experiments and observational data.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Design and productionize causal ML systems to influence marketplace decisions across various business verticals.
Build uplift and heterogeneous treatment effect models for customer lifecycle, promotions, and retention.
Develop counterfactual evaluation frameworks for ranking, recommendations, search results, and marketplace interventions.
Create systems that integrate experimentation, observational data, and machine learning to optimize decision-making.
Design surrogate metrics and early indicators to accelerate product development while maintaining marketplace health.
Apply advanced econometric methods like doubly robust estimation, double ML, and off-policy evaluation to production problems.
Translate complex causal models into functional systems for ranking, targeting, budget allocation, and inventory management.
Establish standards for causal reasoning and debugging across the broader machine learning organization.
What we're looking for
Experience in causal inference, econometrics, experimentation, or causal machine learning.
Experience shipping models or decision systems in production for high-scale settings like marketplaces, ads, or search.
Proficiency with methods including doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.
Ability to build reliable pipelines, train models, and evaluate them rigorously as part of a production system.
Strong judgment regarding the trade-offs between randomized experiments, observational estimation, and model-based decisioning.
Ability to connect causal methods to business decisions rather than just optimizing offline metrics.
Ability to collaborate across functions with engineers, economists, data scientists, product managers, and business leaders.
Machine Learning
Python
Golang
TensorFlow
PyTorch
Hugging Face
LLM
GenAI
RAG
Spark
Kafka
Ray
Airflow
BigQuery
Redis
Distributed Systems
NLP
Search Systems
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