Machine Learning Engineer, Causal Inference, Level 5

Snap Inc.

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: BellevueLos AngelesNew YorkPalo AltoSeattle
Salary: $209,000–$313,000 / yr
Posted: 58 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $231k

This role $261k

$169k $328k

below market most similar roles pay here above market

This role pays more than 80% of similar roles. Most pay $207,300–$254,750 — the blue band above. At the midpoint, this role pays about $261k versus about $231k for comparable roles.

Based on 240 similar postings.

Employer

About Snap Inc.

Snap Inc. is a technology and camera company, best known for Snapchat, offering visual communication, augmented reality, and advertising products.

Snap Inc. currently has 103 open roles on FindRole.

Listed pay typically runs $209,000–$313,000 across 93 roles with salary data.

Most-posted roles

View all roles at Snap Inc.

At a glance

TL;DR · Machine Learning Engineer, Causal Inference, Level 5

Role Posting Log in to save

The Machine Learning Engineer, Causal Inference, Level 5 joins the Engineering team to design and build models that quantify causal impact and optimize decision-making for users, advertisers, and the business. The role involves developing and productionizing causal machine learning solutions, such as uplift modeling and heterogeneous treatment effect estimation, using observational and experimental data. Key responsibilities include designing and interpreting A/B tests and quasi-experiments while evaluating technical tradeoffs between model complexity, bias, variance, scalability, and interpretability. The candidate must be proficient in Python and libraries like pandas, NumPy, scikit-learn, CausalML, EconML, and DoWhy. This position solves complex problems by applying statistical thinking and engineering pragmatism to estimate treatment effects through meta learners, propensity score matching, and instrumental variables within production systems.

What does a Machine Learning Engineer earn?

Median $235750 from 323 postings across 48 companies.

See salary data

Skills

Causal Inference Python A/B Testing Machine Learning pandas NumPy scikit-learn CausalML EconML DoWhy Uplift Modeling Propensity Score Matching Instrumental Variables Bayesian Inference Data Science Statistics

What you'll do

Design and build models to quantify causal impact and optimize business decision-making.
Develop and productionize causal machine learning solutions like uplift modeling and treatment effect estimation.
Design, analyze, and interpret A/B tests and quasi-experiments using observational and experimental data.
Evaluate technical tradeoffs between model complexity, bias, variance, scalability, and interpretability.
Conduct code reviews and maintain high engineering standards for scalable, maintainable infrastructure.
Translate technical insights into actionable information for non-technical partners.

What we're looking for

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.
Master’s degree in a technical field plus 4+ years of post-grad machine learning experience.
PhD in a relevant technical field plus 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.
Proficiency in Python and common data/machine learning libraries like pandas, NumPy, and scikit-learn.
Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research (preferred).

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