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# Machine Learning Engineer, Causal Inference, Level 5

- **Company:** Snap Inc.
- **Location:** Bellevue, WA, United States
- **Experience:** Experienced
- **Salary:** $209,000.0 - $313,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Code Review, Experimental Data, Python (Programming Language), Machine Learning, NumPy, Pandas, Build Management, Scikit Learn, Information Technology, Software Library
- **Published:** August 30, 2026
- **Apply:** https://dejobs.org/x/x/59E97E4DCB084670AA7C957872161144/job/

## About the Role

* Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)
 * Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure
 * Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalM etc.)
 * Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism
 * Comfortable working independently and collaborating across cross-functional teams
 * Strong communication and mentorship skills; able to translate technical insights for non-technical partners, * Bachelor's degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
 * 5+ years of post-Bachelor's experience in machine learning, with hands-on experience in causal inference or experimentation; or Master's degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience
 * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques
 * Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems, * Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research
 * Experience with causal inference libraries such as CausalML, EconML or DoWhy
 * Background in deploying models in production settings and working with ML or experimentation infrastructure
 * Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty

## Description

* Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business
 * Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
 * Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies
 * Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
 * Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
 * Contribute to rapid iteration cycles while ensuring methodological rigor


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