Full Stack AI Systems Engineer

Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish. The people here at Apple don’t just build products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Manufacturing Systems and Infrastructure (MSI) team is an engineering organization under the Product Operations org. MSI is responsible for the design, development, and maintenance of systems tools, services, and applications required to efficiently run manufacturing operations at scale across global factory sites.

Description

As a Full Stack AI systems Engineer on the MSI team, you will design, build, and evolve web applications and services that empower Manufacturing Operations. You will work closely with engineers to understand their workflows and challenges, translate those needs into effective technical solutions, and take features from early concept through production. Your work will span the user experience, backend services, APIs, data flows, and AI capabilities that power these tools. We care deeply about the user experience and think in systems: how components fit together, where the right abstractions belong, and how today’s decisions shape tomorrow’s roadmap. You will apply AI thoughtfully as part of the overall product experience and use modern development tools, including LLM-powered tools, to improve delivery while maintaining a high bar for quality, reliability, and architecture. Because this work involves close collaboration with our users and partners, the ideal candidate combines strong engineering judgment with curiosity, communication, and ownership.

Minimum Qualifications

Bachelor’s degree in Computer Science or a related field, or equivalent practical experience. 3+ years of experience delivering production software through the full software development lifecycle, from requirements and system design to implementation, testing, deployment, and ongoing support. Strong foundation in software engineering principles, including architecture, API and data design, testing, debugging, security, reliability, performance, and maintainability. Experience building full stack applications and the ability to work effectively across user interfaces, backend services, integrations, and data systems. Experience using modern development tools, including AI-assisted coding tools, while applying sound engineering judgment to review, validate, and improve generated code. Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code. Demonstrated ability to understand complex user workflows, translate them into practical technical solutions, and collaborate across teams to deliver measurable outcomes. Excellent communication and collaboration skills, with the ability to translate technical concepts into clear, business focused insights.

Preferred Qualifications

Bachelor's or Master’s degree in Computer Science or a related field, or equivalent practical experience. Experience delivering production software through the full software development lifecycle, from requirements and system design to implementation, testing, deployment, and ongoing support. A genuine passion for systems architecture thinking, and a habit of reasoning about how components, data, and services fit together at scale Experience using AI coding tools such as Claude Code or Continue to enhance the development workflow and accelerate delivery while maintaining code quality and architectural standards Experience integrating LLMs or ML services into production applications, for example prompt design, retrieval augmented generation, agentic workflows, or model backed features Familiarity with message and job queues such as RabbitMQ, Kafka, and with distributed systems Experience with cloud platforms and containerization tools such as Docker and Kubernetes Strong understanding of UX and UI design principles.