Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company operates Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc. , a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji , Saturn, and other digital services. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We’re looking for a Machine Learning Engineer to join Snap Inc! What you’ll do: 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 Knowledge, Skills & Abilities: 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, NumPy, 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 Minimum Qualifications: 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 year 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 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 such as CausalML, EconML or DoWhy Background in deploying models in production settings and working with ML or experimentation infrastructure 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 If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information . Default Together Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4 days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, 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. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable). Our Benefits : Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! Compensation In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future. Zone A (CA, WA, NYC) : The base salary range for this position is $209,000-$313,000 annually. Zone B : The base salary range for this position is $199,000-$297,000 annually. Zone C : The base salary range for this position is $178,000-$266,000 annually. This position is eligible for equity in the form of RSUs.

Requirements

Must-haves

We’re looking for a Machine Learning Engineer to join Snap Inc!
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
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, NumPy, 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
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

Nice-to-haves

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
Background in deploying models in production settings and working with ML or experimentation infrastructure
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
If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information .

Application prep

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Keywords to mirror

machine learningpythonml

Platform: workday
Effort: long

Benefits

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Parental Leave