AI Quality & Test Automation Engineer

Summary

The Apps Services team elevates the development and delivery process that ships code to billions of users worldwide. We build tools and systems that drive efficiency across every stage of the pipeline — from commit to customer. As an AI Quality & Test Automation Engineer, you'll be at the forefront of applying AI to proactively hunt edge cases, surface anomalies, and raise the quality bar for AI-powered features relied upon by teams across the organization.

Minimum Qualifications

5+ years of experience in software development or QA, spanning full-stack, frontend, backend, or integrated QA roles 1+ years of hands-on experience leveraging AI tools in daily engineering workflows to automate tasks and build automated systems Proficiency in one or more scripting languages used for system automation or web development, such as Python, Ruby, or Swift Demonstrated ability to work effectively with observability and logging tooling in Kubernetes-based environments Strong cross-team communication skills with a track record of collaborating effectively with partner engineering organizations Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or we may consider an equivalent practical experience

Preferred Qualifications

Swift application development experience, with familiarity building native or server-side Swift tools Proven experience meeting with stakeholders to distill ambiguous requests into well-defined workflows, engineering requirements, and measurable outcomes Familiarity with CI/CD pipeline architecture and integrating quality gates into automated delivery workflows Experience building internal developer tooling or platform engineering systems used at organizational scale

Description

In this role, you'll design and build intelligent, automated systems that validate infrastructure changes, detect anomalies, and continuously improve the quality of AI features used by customer teams. You'll also help establish the foundational layer of a growing suite of internal applications aimed at reducing critical outages and accelerating engineering workflows. Day to day, you'll collaborate closely with cross-functional partners, translating real operational pain points into elegant, scalable automation solutions.

Responsibilities

Design and implement AI-driven test automation systems that proactively identify edge cases, surface anomalies, and validate infrastructure changes at scale Build and maintain full-stack tooling and internal applications that improve development and delivery efficiency across the Applications organization Integrate observability and logging solutions within Kubernetes environments to monitor system health and flag quality regressions early Collaborate with cross-functional engineering teams to understand quality gaps and translate them into automated workflows and test strategies Leverage scripting languages such as Python, Ruby, or Swift to automate repetitive processes and accelerate quality validation pipelines Partner with stakeholders to gather requirements, distill complex requests into actionable engineering solutions, and communicate progress clearly Contribute to establishing best practices and foundational patterns for AI-assisted quality engineering across the team