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.