Software Engineer, Backend, Level 4
Snap Inc.
Los Angeles, CA, US
Onsite
2026-06-21
Announced salary
$133k–$235k
Market rate in Los Angeles : $102K - $183K (median $142K) · BLS OEWS 2025
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’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.
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 Backend Engineer to join Snap Inc!
What you’ll do:
* Design, implement, and operate our most critical and scalable services \- ranging from user identity services, friend graph, and our core persistence layer
* Work across teams to understand product requirements, evaluate trade\-offs, and deliver the solutions needed to build innovative products
* You evaluate, appropriately test, and debug your work, striving for high quality
* Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management
* Utilize AI tools and high velocity engineering workflows to design and ship scalable services while upholding rigorous standards for code correctness, security, and production ready quality code
Knowledge, Skills \& Abilities:
* Experience with backend services or distributed systems
* Proven track record of operating highly\-available systems at significant scale
* You can independently execute on medium sized features, taking a few weeks and multiple PRs to complete
* You understand
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