Causal ML Engineer: Uplift Modeling & Experiments

Snap Inc.; Palo Alto (CA); On-site; USD 178,000 - 313,000; Full time

Benefits offered by this job

Paid parental leave

Medical coverage

Mental health support

Equity RSUs

Job summary

Snap Inc. is seeking a Machine Learning Engineer to design and productionize causal ML solutions that quantify impact and drive value for users and advertisers.

You will work on uplift modeling, heterogeneous treatment effect estimation, and design experiments with product and engineering partners, balancing model performance with scalability and interpretability. Strong scripting in Python and collaboration across cross-functional teams are essential as you contribute to rapid iteration while

Qualifications

• Bachelor’s degree in computer science, statistics, economics, or related field, or equivalent practical experience.
• 5+ years of post-Bachelor’s experience in machine learning with hands-on causal inference or experimentation; or PhD with 2+ years.
• Demonstrated experience building models to support product decision-making through causal techniques.
• Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems.

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 ML solutions using observational and experimental data.
• Design, analyze, and interpret A/B tests and quasi-experiments with product and engineering partners.
• Evaluate 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.

Skills

Causal inference

A/B testing

Python

Statistical thinking

Experimentation

Communication

Education

Bachelor’s degree in CS/Statistics/Economics

MS/PhD in a technical field

Equivalent practical experience

Tools

CausalML

EconML

DoWhy

scikit-learn

Job description

Snap Inc. is seeking a Machine Learning Engineer to design and productionize causal ML solutions that quantify impact and drive value for users and advertisers.

You will work on uplift modeling, heterogeneous treatment effect estimation, and design experiments with product and engineering partners, balancing model performance with scalability and interpretability. Strong scripting in Python and collaboration across cross-functional teams are essential as you contribute to rapid iteration while