Software Engineer, Quality Platform

Airbnb Airbnb · Consumer · Brazil · Software Engineering

Software Engineer role focused on building AI-powered quality systems and AI-native quality workflows within Airbnb's engineering lifecycle. The role involves designing and implementing AI agents for testing, improving developer productivity through intelligent automation, and integrating AI capabilities into development environments.

What you'd actually do

  1. 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.
  2. Advance AI-Native Quality: Move beyond traditional automation by building systems that continuously adapt test coverage based on code changes and system behavior.
  3. 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.
  4. 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.

Skills

Required

  • 4+ years of software engineering experience in high-scale environments
  • building platforms, infra, or developer/quality tooling
  • Experience with testing frameworks, CI/CD pipelines, developer experience tooling, or quality engineering platforms.
  • Experience building both client and server-side systems.
  • Understanding of distributed systems, CI/CD workflows, and large-scale software architectures.
  • Ability to navigate ambiguity and design practical, scalable solutions.
  • Strong ability to collaborate across teams and explain complex concepts clearly.

Nice to have

  • exposure to or strong interest in AI/ML or LLM-based systems
  • Hands-on experience building applications using LLMs (prompting, APIs, RAG, evaluation, or similar)

What the JD emphasized

  • AI-powered quality systems
  • AI-native quality workflows
  • AI Agents for the Testing Lifecycle
  • AI-Native Quality
  • AI into Engineering Workflows
  • Applied AI
  • agentic E2E validation
  • agentic execution
  • agentic surface discovery
  • test case generation
  • coverage maintenance
  • prompt engineering
  • RAG
  • building applications using LLMs
  • prompting
  • APIs
  • RAG
  • evaluation

Other signals

  • AI-native quality workflows
  • AI agents for the testing lifecycle
  • LLMs, intelligent automation, and data-driven systems into the testing lifecycle