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# Software Engineer

- **Company:** Airbnb
- **Location:** Trust, NC, United States (Remote available)
- **Experience:** Expert
- **Salary:** $162,000.0 - $186,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Architectural Patterns, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Ruby on Rails, Ruby, Scala (Programming Language), Web Applications, Backend, Data Pipelines
- **Published:** August 20, 2026
- **Apply:** https://www.workingnomads.com/job/go/1804324/

## About the Role

* 3+ years of industry experience on building systems at scale, inclusive MS or PhD in relevant fields
 * Passion for building user-facing products or large backend systems.
 * Experience in any of the following: Ruby/Ruby on Rails, Java, JavaScript, Python, Scala
 * Ability to write high performance production quality code
 * Exposure to architectural patterns of large, high-scale web applications, such as well-designed APIs, high volume data pipelines, and efficient algorithms.
 * Experience or desire to work collaboratively in cross-functional teams with design, product, data science, and research partners.
 * Ability to effectively communicate with non-technical stakeholders such as legal and operations.
 * Experience with the Trust and Risk domain is a plus.

## Description

As a Software Engineer on the Listing Integrity team, you will be working with data scientists, designers, product managers, and customer service operations to innovate new ways we can stop bad actors in the ever evolving fraudulent listing creation and financial losses associated with it by collaborating across team boundaries.

On this team, you must have the curiosity to dig deep into various end to end systems in order to understand how and where the fraud occurs. Your curiosity will be rewarded with finding projects that have outsized impacts on decreasing fraud losses and protecting Airbnb users from bad experiences while on vacation.

A Typical Day:

 * Work with large scale back end systems to detect fraudsters, using rules and connecting to productionalized machine learning models.
 * Work collaboratively with cross-functional partners including machine learning engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for fraud detection and mitigation.
 * Building out tooling and automation for operations to more effectively reduce fraud.
 * Building user challenges to prevent fraudulent listings while reducing good user impact.
 * Examples include: location verification user challenges, automating fraud investigation, improving operations tooling, building data pipelines for machine learning, and reducing fraud at scale.


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