Machine Learning Engineer, Causal Inference, Level 5

Snap; New York (NY); Hybrid; USD 199,000 - 313,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Emotional and mental health support programs

Job summary

Snap is seeking a Machine Learning Engineer to design and build models that optimize decision-making and drive value for users and advertisers. This role involves developing causal machine learning solutions and interpreting A/B tests while collaborating with cross-functional teams.

The ideal candidate has extensive experience in machine learning, strong Python skills, and a background in causal inference or experimentation. Snap offers a competitive salary range based on location, skills, and qualifications.

Qualifications

• 5+ years of post-Bachelor’s experience in machine learning, with hands-on experience in causal inference.
• Experience designing and analyzing online experiments (A/B tests).
• Advanced degree (MS/PhD) in a quantitative field preferred.

Responsibilities

• Design and build models for causal impact and decision-making.
• Develop causal machine learning solutions using observational and experimental data.
• Conduct code reviews and maintain high engineering standards.

Skills

Causal inference

Python proficiency

Data science

Statistical thinking

Communication skills

Education

Bachelor’s degree in computer science, statistics, economics, or a related technical field

Tools

CausalML

pandas

NumPy

scikit-learn

Job description

We’re looking for a Machine Learning Engineer to join Snap Inc.

Responsibilities

• 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 (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+ 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

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
• Experience applying causal inference in domains like personalization, ad or marketplace dynamics

Accommodations

If you have a disability or special need that requires accommodation, please let us know.

Default Together Policy

We practice a “default together” approach and expect our team members to work in an office 4+ days per week.

Equal Opportunity Employer

Snap is proud to be an equal opportunity employer, 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).

Benefits

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 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): Base salary range $209,000‑$313,000 annually.

Zone B: Base salary range $199,000‑$297,000 annually.

Zone C: Base salary range $178,000‑$266,000 annually. This position is eligible for equity in the form of RSUs.