Snap

Machine Learning Engineer, Causal Inference, Level 5

Los Angeles, California

Sponsorship not specified$209k-$313kDetected 21 days ago

PythonExpressCode ReviewMachine Learningscikit-learnPandasNumPyData ScienceStatisticsA/B TestingResearchCommunicationMentoring

About the role

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.
We move fast, with precision, and always execute with privacy at the forefront.

Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business
Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies
Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
Strong communication and mentorship skills; able to translate technical insights for non-technical partners
Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day.
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.

Bachelor's degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)

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
Experience applying causal inference in domains like personalization, ad or marketplace dynamics
To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

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

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.
The base salary range for this position is $209,000-$313,000 annually.
The base salary range for this position is $199,000-$297,000 annually.
The base salary range for this position is $178,000-$266,000 annually.

This position is eligible for equity in the form of RSUs.
Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
We're looking for a Machine Learning Engineer to join Snap Inc!
EOE, including disability/vets.

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.
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.
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.

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).

This listing is sourced directly from Snap's careers page and normalized into a canonical job model.