Software Engineer, Quality Platform
Airbnb
| Company | Airbnb |
| Category | Engineering |
| Location | Brazil |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 21 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join
The Quality Platform team is at the heart of Airbnb’s mission to deliver a seamless, high-quality experience for millions of hosts and guests. We don’t just find bugs — we build the systems that prevent them.
Our team sits at the intersection of Quality Engineering, Infrastructure, and Applied AI . We are evolving how software quality is built by integrating LLMs, intelligent automation, and data-driven systems into the testing lifecycle. You will join a high-impact group of engineers focused on building AI-powered quality systems that scale across one of the world’s most complex codebases.
The Difference You Will Make
As a Software Engineer , you will play a key role in building and evolving AI-native quality workflows across Airbnb. You will design systems that apply AI directly to the testing lifecycle , helping developers and quality engineers build, validate, and ship high-quality software at scale.
In this role, you will:
Build AI Agents for the Testing Lifecycle: Design and implement AI agents that support developers and quality engineers across the testing lifecycle — from test case generation and evolution to automation and maintaining test coverage as the codebase changes.
Advance AI-Native Quality: Move beyond traditional automation by building systems that continuously adapt test coverage based on code changes and system behavior.
Improve Developer and QE Productivity: Identify bottlenecks in testing and CI/CD workflows, and solve them through intelligent automation that reduces manual effort and accelerates feedback loops.
Integrate AI into Engineering Workflows: Embed AI capabilities into real development environments, enabling both developers and quality engineers to receive contextual, actionable insights during development and testing.
A Typical Day
Hands-on Development: Build end-to-end systems and tools (TypeScript, Swift, Kotlin, Java, Python) that will power Airbnb’s AI quality platform. This includes services, developer tooling, and UI surfaces that integrate AI into testing frameworks/infrastructure, CI/CD pipelines, and everyday developer workflows.
Applied AI: Develop AI-assisted capabilities such as context-aware agentic E2E validation on pull requests, agentic execution of business-critical flows as quality gates, agentic surface discovery and exploratory testing, and systems for test case generation, and coverage maintenance.
System Design: Participate in the design of scalable AI-driven systems operating within large-scale engineering environments.
Cross-Functional Collaboration: Work closely with Mobile, Infrastructure, Product Engineering, and Quality Engineering teams to integrate AI into real-world development workflows.
Experimentation & Iteration: Explore approaches such as prompt engineering and RAG (Retrieval-Augmented Generation) to improve system effectiveness and adaptability.
Mentorship: Provide technical guidance and help raise the bar for applying AI in engineering systems across the team.
Your Expertise
Experience: 4+ years of software engineering experience in high-scale environments, ideally building platforms, infra, or developer/quality tooling, with exposure to or strong interest in AI/ML or LLM-based systems.
Quality / DexEx/ Tooling Systems : Experience with testing frameworks, CI/CD pipelines, developer experience tooling, or quality engineering platforms.
Applied AI / LLMs: Hands-on experience building applications using LLMs (prompting, APIs, RAG, evaluation, or similar)