Machine Learning Engineer, Causal Inference, Level 5

Snap Inc.•Los Angeles, CA

30d•$178,000 - $313,000•Hybrid

About The Position

Snap Inc. is seeking a Machine Learning Engineer specializing in Causal Inference to join their team. This role involves designing and building models to quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business. The engineer will develop and productionize causal machine learning solutions using observational and experimental data, design, analyze, and interpret A/B tests and quasi-experiments, and collaborate with product and engineering partners to shape experimentation strategies. Responsibilities also include evaluating technical tradeoffs, conducting code reviews, maintaining high engineering standards, building scalable infrastructure, and contributing to rapid iteration cycles with methodological rigor.

Requirements

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

Nice To Haves

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

Responsibilities

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