Machine Learning Engineer, Causal Inference, Level 5

Snap is hiring a Machine Learning Engineer focused on causal inference to design, build, and productionize causal machine learning models. This role also supports experimentation strategy and rigorous evaluation of technical tradeoffs across modeling, measurement, and decision-making.

Location and Work Style

• Los Angeles, CA (onsite)
• Snap uses a “default together” approach
• Team members are expected to work in an office 4+ days per week

Salary and Compensation

• Salary range: USD 178,000 - 313,000 per year
• Zone A (CA, WA, NYC): $209,000 - $313,000 base
• Zone B: $199,000 - $297,000 base
• Zone C: $178,000 - $266,000 base
• Eligible for equity in the form of RSUs
• Starting pay may be negotiable within the salary range

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 such as uplift modeling and heterogeneous treatment effect estimation using observational and experimental data
• Design, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategies
• Evaluate technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
• Perform code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
• Contribute to rapid iteration cycles while ensuring methodological rigor

Requirements

• Strong causal inference understanding, including modern approaches to estimating treatment effects (for example: 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 libraries such as pandas, NumPy, scikit-learn, and causal inference tooling
• Ability to solve open-ended problems with a blend of statistical thinking and engineering pragmatism
• Comfort working independently and collaborating with cross-functional teams
• Strong communication and mentorship skills, including translating technical insights for non-technical partners
• Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
• Experience profile: 5+ years post-Bachelor’s in machine learning with hands-on causal inference or experimentation, or Master’s + 4+ years post-grad machine learning experience, or PhD + 2 years post-grad machine learning experience
• Demonstrated experience building causal models to support product decision-making and policy evaluation
• Experience designing and analyzing online experiments and leveraging causal ML in production systems

Technologies

• Python
• pandas
• NumPy
• scikit-learn
• CausalM
• CausalML
• EconML
• DoWhy

Benefits

• Paid parental leave
• Comprehensive medical coverage
• Emotional and mental health support programs
• Compensation packages intended to align with Snap’s long-term success

Preferred Qualifications

• Advanced degree (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) versus ghost ad methodologies, and trade-offs between frequentist and Bayesian inference for decision-making under uncertainty
• Experience applying causal inference in domains such as personalization and ad or marketplace dynamics