Software Engineer - Agent Development

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something - you’ll add something.

Join us, the team that serves as Apple’s nerve center: Apple Information Systems and Technology group! We are looking for Software QE Engineers, who will work closely with other Software Engineers, Project & Product Managers, Product Designers, and other internal & external partners.

There are countless ways you can contribute here, from working on apple.com to software for digital & physical Apple Stores, from software for Apple Care to Apple-wide systems for identity, operations, recruiting, and people management, from software for Apple data centers to Apple-wide data processing & AI/ML systems.

Apple's IS&T team builds the tools, platforms, and services that power the people who build Apple. Our Customer Systems group is reimagining how Apple engineers work through intelligent, autonomous systems - and we're looking for an Agentic AI Developer to help lead that effort.

Description

We are looking for a talented Agentic AI Developer to join Apple's IS&T Customer Systems team. In this role, you will design, build, and operate intelligent autonomous agent systems end-to-end - both client-side and server-side. You will architect agents that reason, plan, remember, and act reliably across long-running workflows, with deep attention to memory design, multi-agent orchestration, observability, and maintainability.

This is a deeply technical engineering role with a high bar for production quality. You will own the full lifecycle of agentic systems: from agent architecture and memory design, to tool integration, observability, and safe deployment. While the primary focus is agent engineering, a solid understanding of the Java tech stack, React, and TypeScript is also important, as you will work across both server-side and client-side surfaces.

You will collaborate closely with engineers, product managers, and platform teams across Apple to bring AI capabilities into the developer tools our teams depend on every day.

Minimum Qualifications

4+ years of software engineering experience, with at least 2 years focused on AI/ML systems, LLM integrations, or autonomous agent development

Hands-on experience designing and implementing multi-agent orchestration systems and agent loop patterns (ReAct, Plan-and-Execute, or similar)

Strong proficiency in Java (Spring Boot) and TypeScript/React for building full-stack agentic applications

Solid understanding of LLM fundamentals: prompt engineering, context window management, function calling, structured outputs, and tool use

Experience building RAG pipelines and working with vector databases .

Demonstrated ability to implement agent safety guardrails including prompt-injection defences and output schema enforcement

Experience with observability tooling (OpenTelemetry or equivalent) applied to distributed, AI-driven systems

Bachelor's degree in Computer Science, Software Engineering, or a related technical field .

Preferred Qualifications

Experience implementing or consuming MCP (Model Context Protocol) servers and clients

Familiarity with model routing and fallback strategies across multiple LLM providers

Reactive programming in Java - Project Reactor, WebFlux - for streaming LLM responses

Python proficiency for agent prototyping and evaluation pipeline scripting

Hands-on experience with cloud AI platforms .

Experience with agent sandboxing and safe code execution environments

Awareness of fine-tuning, LoRA, and small-model distillation techniques for agent specialisation