Snap
Quality Engineer, Level 4
$133K–$235K
Los Angeles, California · Full-time · Posted Oct 23, 2026
- Experience
- 3+ years post-Bachelor’s in software testing and/or development; or a technical Master’s degree plus 2+ years post-grad testing or development; or a PhD in a relevant technical field.
- Education
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience.
About the job
Own end-to-end quality, test strategy, automation, and release readiness for Snap’s Ad Client advertising experiences across teams and surfaces.
What you'll do
- Define and drive testing strategy, prioritizing high-risk user journeys and critical end-to-end paths.
- Build and maintain automation for compatibility, accessibility, privacy, compliance, and localization; diagnose defects and coordinate resolution.
- Own release readiness and quality sign-off, review code and tests, and participate in on-call rotations.
What they ask for
- Proficiency in object-oriented programming with Java, Kotlin, Swift, Objective-C, or similar.
- Experience with client or mobile test automation frameworks and mobile application test coverage across devices.
- Hands-on use of AI in engineering workflows, with judgment about when human validation is needed.
- Bachelor’s degree in a relevant technical field or equivalent practical work experience; experience alternative: 3+ years post-Bachelor’s in testing or development, or a technical Master’s plus 2+ years post-grad testing or development, or a relevant technical PhD.
Nice to have
- Experience working in a distributed product-team environment.
- Experience with Amazon Web Services (AWS) and/or Google Cloud, or continuous integration/delivery systems.
Pay
$157,000-$235,000 annually USD — base salary; $149,000-$223,000 annually USD — base salary; $133,000-$200,000 annually USD — base salary
Tools
Java, Kotlin, Swift, Objective-C, XCTest/XCUITest, Selenium WebDriver, Appium, Espresso
AI focus
Use AI to accelerate engineering work and improve efficiency; apply judgment and human validation, while focusing quality efforts on cross-team and higher-risk experiences as AI-assisted testing covers routine feature correctness.
Model-written summary · the original posting is authoritative