Machine Learning Engineer, Causal Inference, Level 5

We're looking for a Machine Learning Engineer to join Snap Inc!

- 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

- 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

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

Zone A (CA, WA, NYC) : The base salary range for this position is $209,000-$313,000 annually.

Zone B : The base salary range for this position is $199,000-$297,000 annually.

Zone C : The base salary range for this position is $178,000-$266,000 annually.