Job Description

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 revolutionised 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.

As a Manufacturing AI Application Engineer with the Manufacturing Systems and Infrastructure team, you will serve as a subject-matter expert in edge AI deployment and industrial hardware integration. You will drive the design and development of high-performance, AI-integrated test stations that power our global factory floors — leveraging machine learning, computer vision, and industrial communication protocols to support manufacturing infrastructure at scale. You will own complex, cross-functional initiatives end-to-end — from architecture and design through New Product Introduction ramp-up and post-deployment reliability — while setting engineering standards and best practices for the team.

- Architect and develop the core systems that runs on physical manufacturing test stations, seamlessly integrating AI models, UI/UX for factory operators, and hardware control logic.
- Deploy and optimize machine learning models and computer vision algorithms directly onto edge devices (e.g., Mac minis, industrial PCs) to automate visual inspection, fault detection, and functional testing.
- Develop robust systems to control and communicate with physical test fixtures, sensors, PLCs, and industrial automation equipment using standard protocols (RS232, UART, TCP/IP).
- Lead the rollout and scaling of these intelligent test systems across regional factories, ensuring smooth integration with the existing factory network and Manufacturing Execution Systems (MES) and Data collection systems.
- Act as the technical lead during New Product Introductions, debugging complex integration issues between the AI Tools, the edge hardware, and the physical test fixtures during the ramp-up phase.
- Utilize data analytics and AI-driven insights from the test stations to optimize test sequences, reduce cycle times, and identify root causes of manufacturing defects.
- Partner with global AI development teams, hardware engineers, and regional factory operations to translate complex test requirements into robust, deployable solutions.

Preferred Qualifications

Experience working across global manufacturing sites, including direct floor-level engagement with production lines
Self-motivated with an entrepreneurial spirit, excellent time management, and the strong written/verbal communication skills necessary to explain complex technical trade-offs.
Experience in performance management and team development, fostering a culture that embraces new technologies and AI adoption.

Minimum Qualifications

BS or MS in Software Engineering is required.
Minimum 4 years of relevant work experience in manufacturing software development, factory automation, or industrial IoT systems.
Adept at utilizing GenAI platforms to drive process improvements, analyze data, and accelerate project delivery
Strong proficiency in Python (specifically for ML/AI integration), Embedded C/C++, Perl, Ruby, and Shell Scripting (Bash).
Proficient in leveraging Generative AI coding assistants (e.g., GitHub Copilot, Cursor) to accelerate development, refactor code, and streamline debugging used for hardware control, automation, and AI model integration.
Excellent working knowledge of industrial communication and networking protocols (TCP/IP, MQTT, RS232, UART).
Hands-on experience deploying machine learning models, specifically computer vision/inspection models, to edge hardware in real-time environments.
Proven experience developing systems that interfaces directly with physical hardware, sensors, test instruments, or robotics.
Exceptional ability to debug complex, multi-disciplinary systems (where systems, networking, and physical mechanics intersect) on a live manufacturing line.