Machine Learning Engineer, Causal Inference, Level 5

Snap Inc. is seeking a Machine Learning Engineer to develop causal inference and experimentation solutions that drive product and business decisions.

• Design and build models that quantify causal impact, optimize decision‑making, and drive value for users, advertisers, and the business
• Develop and product ionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
• Design, analyze, and interpret A/B tests and quasi‑experiments; collaborate closely with product and engineering partners to shape experimentation strategies
• Evaluate technical trade‑offs 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

• Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)
• Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure
• Proficient in Python and common data/machine‑learning libraries (e.g., pandas, Num Py, scikit‑learn, CausalM etc.)
• Skilled at solving open‑ended problems with a mix of statistical thinking and engineering pragmatism
• Comfortable working independently and collaborating across cross‑functional teams
• Strong communication and mentorship skills; able to translate technical insights for non‑technical partners

• Bachelor’s degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
• 5+ years of post‑Bachelor’s experience in machine learning, with hands‑on experience in causal inference or experimentation; or Master’s degree in a technical field + 4+ years of post‑grad machine learning experience; or PhD in a relevant technical field + 2 years of post‑grad machine learning experience
• Demonstrated experience building models to support product decision‑making and policy evaluation through causal techniques
• Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems

• Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research
• Experience with causal inference libraries such as CausalML, EconML, or Do Why
• Background in deploying models in production settings and working with machine‑learning or experimentation infrastructure
• Deep understanding of experimentation nuances, including intent‑to‑treat vs. ghost ad methodologies, and the trade‑offs between frequentist and Bayesian inference for decision‑making under uncertainty
• Experience applying causal inference in domains like personalization, ad or marketplace dynamics

• Paid parental leave
• Comprehensive medical coverage
• Emotional and mental health support programs
• Compensation packages that share in Snap’s long‑term success, including eligible equity in the form of RSUs

• Base salary ranges vary by pay 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
• Starting pay may be negotiated within the salary range and subject to market conditions

Snap Inc. is an equal‑opportunity employer. We are committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical or mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth, age, sexual orientation, military or veteran status, or any other protected classification in accordance with applicable federal, state, and local laws.

EOE, including disability/vets. Snap considers qualified applicants with criminal histories in a manner consistent with applicable law.

Instructions for accommodations:
If you have a disability or special need that requires accommodation, please provide us with information through the provided form.