ML Model Integration Platform Eng

Join the Apple Service Engineering (ASE) team and drive innovation that matters! The ASE team builds and provides systems and infrastructure that fuel Apple’s services. As part of this team, you will be responsible for building and integrating technologies that enhance people’s lives. We are also tasked with enabling Apple Intelligence and Private Cloud Compute in the cloud. We’re looking for a senior engineer who can help integrate Large Language Models into our software ecosystem.

Description

This role is for a Platform Engineer specializing in Apple Intelligence and Private Cloud Compute. Your responsibilities will include architecting, designing, and delivering the systems and platform components that integrate Large Language Models (LLMs) and other AI models into Apple's products. This role establishes the foundation that enables teams to safely and effectively deploy ML-powered features. You will ensure the systems surrounding the models are robust, scalable, and easy for others to build on, becoming a key partner in the organization's delivery of AI-driven experiences. The ideal candidate is a systems thinker who can navigate fast moving requirements while maintaining a long-term vision for resilient and scalable solution.

Minimum Qualifications

5+ years of software engineering experience in building and operating production systems. Strong background in distributed systems and an ability to reason about scale, concurrency, and failure modes. Proven experience designing and implementing internal tools, automation, and service-level components. Ability to collaborate closely across teams and influence decisions through clarity, empathy, and technical depth. A holistic mindset—seeing beyond individual components to understand and communicate system-level trade-offs. Comfortable working in dynamic, fast-changing environments where you help create structure, not wait for it. Proficient in scripting and programming languages such as C/C++, Python, SQL, Shell Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science or related field

Preferred Qualifications

Understanding and practical experience with containerization technologies (Docker) and orchestration platforms (Kubernetes). Solid background in one or more major cloud providers (AWS, GCP, Azure), including familiarity with compute, storage, networking, and security services relevant to ML workloads.