Machine Learning Engineer (Causal Inference)

• 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 (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 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

Benefits

• Up to 28 weeks of parental leave and return-to-work program
• Adoption, surrogacy, infertility, and fertility preservation benefits
• Backup child care coverage, center-based care support, & caregiver assistance
• Dependent Daycare Flexible Spending Account
• For Snap, employees taking care of loved ones with complex, chronic, or ongoing care needs (i.e. parent with Dementia, spouse with Cancer, child with Autism), your family can save time, money, & stress with Wellthy
• Services to support your path to parenthood & beyond - fertility support & family planning via Carrot, SNOO, Hinge Health
• Short-term disability, long-term disability, basic life insurance, and AD&D insurance
• Lactation rooms (based on office location) and breastmilk shipping coverage
• Comprehensive medical coverage, including PPO, HDHP with HSA, and HMO options
• Dental coverage, including orthodontia benefits
• Vision coverage, including frame and contact allowance and LASIK benefits
• Free membership to One Medical for primary and urgent healthcare services
• Business Travel Insurance
• Gym perks and discounts
• Team fitness classes and races
• Sports leagues
• Cooking and nutritional workshops
• Well-being reimbursement
• Unlimited access to virtual physical therapy with Anthem
• 15 sessions per year of mental health for you + dependents through Lyra
• Generous time off and leave programs. In the US, this includes the following:
• Up to 3 weeks (120 hours) of vacation time and 1 floating holiday for non-exempt team members
• 15 sick and safe days per year
• 12 paid holidays per year
• Meditation and yoga classes
• Social gatherings, team outings, and volunteering programs
• Speaker series, classes, and subscriptions to educational programs
• Snap Inc. provides a 401(k) plan that allows you to save on a pre-tax, Roth, and after-tax basis for your retirement (yes, we even have the Mega backdoor option!) Snap will also match 100% up to 3% of your contribution and then 50% on your 4th and 5th%
• Flexible Spending Accounts for healthcare, daycare and commuter
• Health Savings Account with employer contributions
• Rocket Lawyer memberships
• Financial education programs
• Snap exclusive discounts and perks
• Compensation packages that let you share in Snap’s long-term success!- Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)
• Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalM etc.)
• 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
• Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism
• 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+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience
• Comfortable working independently and collaborating across cross-functional teams
• Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems
• Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure
• Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques
• 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 DoWhy
• Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty
• Background in deploying models in production settings and working with ML or experimentation infrastructure