Job Description
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.
Manufacturing Systems and Infrastructure (MSI) team is an engineering organisation under the Product Operations org. MSI is responsible for the design, development and maintenance of system tools, services and applications required to efficiently run manufacturing operations at scale across global factory sites.
As an OnDevice Application Engineer with the MSI team, you will serve as a subject-matter expert in native application development and on-device AI/ML integration. You will drive the architecture and development of high-performance native applications that power our OnDevice ecosystems — leveraging AI, the iOS frameworks, and Apple-specific APIs to support manufacturing infrastructure at scale. You will own complex, cross-functional initiatives end-to-end — from architecture and design through delivery and post-deployment reliability — while setting engineering standards and best practices for the team.
Preferred Qualifications
Excellent communication skills - ability to articulate technical trade-offs clearly to both engineering teams and non-technical manufacturing stakeholders
Practical experience using AI-centric scripting languages (Python) alongside GenAI tools to automate data handling, synthesise test data and streamline model integration workflows
Experience integrating applications with REST/GraphQL web services, event-driven backends and both cloud-based and local AI inference engines - including LLM APIs and computer vision model integration
Solid command of release engineering and SDLC best practices, with experience using AI tools to optimise CI/CD pipelines and manage the complex nuances of deploying machine learning model updates alongside application binaries
Demonstrated experience in quality engineering - unit, UI and performance testing - leveraging Generative AI to automate test script creation, generate edge-case scenarios and validate AI model accuracy
Familiarity with AI-powered quality and visual testing tools integrated into daily testing workflows to ensure application stability and reduce manual review time
Experience in manufacturing, factory automation or industrial IoT software domains
Published apps on the App Store or a strong portfolio demonstrating technical depth in on-device AI and native platform development
Minimum Qualifications
6+ years of hands-on experience in native application development with a strong focus on iOS and macOS platforms
Expert-level proficiency in Swift and strong working knowledge of Objective-C, with demonstrated capability of leveraging Generative AI coding assistants (e.g., GitHub Copilot, Cursor) to accelerate feature development, code refactoring, and daily programming tasks
Deep expertise in the Apple ecosystem with a proven track record of seamlessly integrating AI/ML models into production-grade applications
Deep expertise in Apple frameworks — UIKit, SwiftUI, Combine, and Foundation — with specialised, hands-on knowledge of Apple’s machine learning stack: Core ML, Vision, Create ML, and Metal for hardware-accelerated edge inference
Deep understanding of multi-threaded system design — GCD, async/await, actors, and thread-safety patterns — critical for running computationally intensive AI models locally without degrading the end-user application experience
Proven ability to define and enforce mobile/desktop performance optimisation strategies covering memory, battery, GPU utilisation, and application launch time in the context of on-device AI workloads
