Machine Learning Engineer, Causal Inference, Level 5

Snap•Santa Monica, CA

6d•Hybrid

About The Position

Snap Inc. is a technology company focused on improving communication through its camera. The company operates Snapchat, Bitmoji, and other digital services. Snap's Engineering teams build technically sophisticated products used by hundreds of millions of users globally. The company values speed, precision, and privacy. This role is for a Machine Learning Engineer specializing in Causal Inference to join the team.

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.
Experience applying causal inference in domains like personalization, ad or marketplace dynamics.

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.