Snap is hiring a Machine Learning Engineer to build causal inference machine learning models for decision-making and experimentation.

Design and develop causal impact models to quantify effects and drive value for users, advertisers, and the business
Build and productionize causal ML solutions using observational and experimental data (including uplift modeling and heterogeneous treatment effect estimation)
Plan, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategies
Assess technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
Perform code reviews, uphold engineering quality standards, and contribute to scalable, maintainable infrastructure
Support rapid iteration cycles while maintaining methodological rigor

Strong foundation in causal inference and modern treatment effect estimation approaches (meta learners, propensity score matching, instrumental variables)
Applied data science experience, including A/B testing, uplift modeling, and experimentation infrastructure
Proficiency in Python and common ML/data libraries such as pandas, NumPy, and scikit-learn
Experience solving open-ended problems with a balance of statistical thinking and engineering execution
Comfort working independently and collaborating across cross-functional teams
Strong communication and mentorship skills, including the ability to explain technical insights to non-technical partners
Education/experience qualification:
- Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
- AND 5+ years post-Bachelor’s experience in machine learning with hands-on causal inference or experimentation
- OR Master’s degree in a technical field plus 4+ years post-grad machine learning experience
- OR PhD in a relevant technical field plus 2 years post-grad machine learning experience
Demonstrated experience building causal models for product decision support and policy evaluation
Experience designing and analyzing online experiments and using causal ML in production systems

Advanced degree (MS/PhD) in statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries such as CausalML, EconML, or DoWhy
Experience deploying models in production and working with ML or experimentation infrastructure
Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and frequentist vs. Bayesian tradeoffs for decision-making under uncertainty
Experience applying causal inference to personalization, ad, or marketplace dynamics

Salary (base) ranges by zone:
- Zone A (CA, WA, NYC): $209,000-$313,000 annually
- Zone B: $199,000-$297,000 annually
- Zone C: $178,000-$266,000 annually
Equity eligibility in the form of RSUs
Starting pay may be negotiable within the salary range for the position
Compensation packages available that let you share in Snap’s long-term success

Machine Learning Engineer, Causal Inference, Level 5

Job Description