Snap is hiring a Machine Learning Engineer to build and productionize causal inference models for measurable impact.

Design and build causal impact models that quantify effects, optimize decisions, and support value creation for users, advertisers, and the business
Develop and productionize causal machine learning approaches such as uplift modeling and heterogeneous treatment effect estimation using observational and experimental data
Plan, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategy
Assess technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
Perform code reviews, uphold engineering quality, and help build scalable, maintainable infrastructure
Drive fast iteration cycles while maintaining methodological rigor

Causal inference expertise, including modern techniques for treatment effect estimation such as meta learners, propensity score matching, and instrumental variables
Applied data science experience covering A/B testing, uplift modeling, and experimentation infrastructure
Proficiency in Python and common data/ML libraries including pandas, NumPy, and scikit-learn, plus causal ML tooling such as CausalM
Ability to tackle open-ended problems by combining statistical thinking with engineering pragmatism
Comfort working independently and collaborating with cross-functional partners
Strong communication and mentorship skills, including translating technical findings for non-technical stakeholders
Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
Experience level aligned to one of the following paths: 5+ years post-Bachelor’s with hands-on causal inference or experimentation; or Master’s + 4+ years post-grad ML; or PhD + 2 years post-grad ML
Demonstrated experience building causal models that support product decision-making and policy evaluation
Experience designing and analyzing online experiments (A/B tests) and using causal ML in production

MS/PhD in a quantitative field such as statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries including CausalML, EconML, or DoWhy
Background 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 tradeoffs between frequentist and Bayesian inference for decision-making under uncertainty
Experience applying causal inference in areas such as personalization, ad, or marketplace dynamics

Base salary range 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
Eligible for equity in the form of RSUs
Starting pay may be negotiable within the salary range
Compensation packages designed to support Snap’s long-term success

Machine Learning Engineer, Causal Inference, Level 5

Job Description