Staff Machine Learning Engineer, Causal Inference at Doordashusa · LumiJobs
senior
$204k–$299k
H1B: no
Responsibilities
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 platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.
You're excited about this opportunity because you will...
Design, build, and productionize causal ML systems that influence real 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 marketplace interventions.
Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.
Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.
Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.
Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.
Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.
We're excited about you because you have...
Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
