Senior Causal ML Engineer: Production & A/B Experiments

Snap Inc.; Los Angeles (CA); On-site; USD 209,000 - 313,000; Full time

Benefits offered by this job

Equity RSUs

Medical coverage

Paid parental leave

Job summary

Snap Inc. is seeking a Machine Learning Engineer to design and productionize causal ML models that quantify impact for users, advertisers, and the business. You will work with cross-functional teams to shape experimentation strategies and ensure rigorous evaluation.

The role requires deep knowledge of causal inference, online experiments, and scalable ML infrastructure, with strong Python skills and the ability to communicate technical insights clearly.

Qualifications

• Strong background in causal inference and treatment effect estimation.
• Hands-on experience with A/B tests and experimentation infrastructure.
• Proficient in Python and ML libraries (pandas, NumPy, scikit-learn).
• Able to translate technical insights for non-technical partners.

Responsibilities

• Design and build models to quantify causal impact and value for users, advertisers, and business.
• Develop and productionize causal ML solutions using observational and experimental data.
• Design, analyze, and interpret A/B tests and quasi-experiments with product/engineering partners.
• Evaluate tradeoffs between model complexity, bias/variance, scalability, and interpretability.
• Conduct code reviews and build scalable, maintainable infrastructure.
• Contribute to rapid iteration with methodological rigor.

Skills

Causal inference

Experimentation

Python

Data analysis

Communication

Cross-functional teamwork

Education

Bachelor's degree in CS/Statistics/Economics or related

Tools

CausalML

EconML

DoWhy

scikit-learn

Job description

Snap Inc. is seeking a Machine Learning Engineer to design and productionize causal ML models that quantify impact for users, advertisers, and the business. You will work with cross-functional teams to shape experimentation strategies and ensure rigorous evaluation.

The role requires deep knowledge of causal inference, online experiments, and scalable ML infrastructure, with strong Python skills and the ability to communicate technical insights clearly.