About the job
At Apple Maps, we're not just building world-class location experiences — we're shaping the future of how people explore and navigate the world. The Maps Client Quality Engineering Intelligence (QEI) team applies AI across the full breadth of QE workflows — from test scheduling and triage to coverage analysis and release readiness — to help QE leads and SDETs ship with confidence. We raise quality and velocity at the same time. Our culture is one of deep collaboration, shared ownership, and a belief that the best solutions come from staying close to the people who use them. If you're excited about applying AI to transform how a large quality organization works, we'd love to have you on the journey.
Responsibilities
Apply AI as a first-class approach across QE workflows — test scheduling, triage, coverage analytics, release readiness, knowledge retrieval. Identify where AI can replace manual effort, prototype solutions, and ship them into production.
Build across the full stack: Python microservices (FastAPI), web applications (Next.js / TypeScript), data pipelines, and AI integrations (LLMs, agents, RAG, MCP).
Own features end-to-end from problem definition through deployment and follow-up.
Partner with QE leads, SDETs, and QE managers to identify gaps, define requirements, and deliver scalable solutions. Automate manual processes by integrating with internal bug tracking, test execution, and source control systems.
Help define how AI and tooling evolve to raise the bar for quality at scale across Maps Client.
Qualifications
Minimum
Strong Python skills with experience building backend services, data pipelines, or APIs.
Experience with web technologies (TypeScript, React/Next.js, or similar frameworks).
Proven ability to ship features end-to-end: requirements through deployment and monitoring.
Solid understanding of REST API design, microservices architecture, and CI/CD.
Experience partnering with QE or automation engineers to define tooling needs and architect solutions.
Foundational understanding of AI concepts — LLMs (e.g. Claude), agents, RAG architectures, prompt engineering, and Model Context Protocol (MCP) — with the ability to apply them to real engineering problems.
Exposure to or experience integrating AI/ML into engineering workflows (intelligent triage, test selection, knowledge retrieval).
Strong analytical, debugging, and communication skills.
Creative problem-solver who can turn ambiguous asks into concrete deliverables.
Bachelor's or Master's degree in Computer Science or equivalent, with 3-6 years of industry experience in software development.
Preferred
Hands-on experience deploying AI/ML in production settings — shipping models, pipelines, or AI-powered features to real users.
Passion for building clean, reusable tools and contributing to platform evolution.
Strong sense of ownership and drive to improve quality at scale.
Background in developer tooling, internal platforms, or test automation frameworks (XCTest, XCUI, or similar).
Familiarity with large-scale engineering platforms (bug tracking, test execution, CI/CD, build systems) and navigating complex enterprise tooling ecosystems.