AIML Software Engineer, On-Device Audio Intelligence, Sensing & Connectivity

Summary

In Sensing & Connectivity, we use on-device sensors and wireless technologies to understand user context — combining sensor data with machine learning to create intelligent, ambient experiences across Apple platforms.
As a Software Engineer on the Location & Motion team, you will design and build the frameworks and system services that enable on-device sensing at scale. You will create algorithms that process real-time signals, extract meaningful context, and deliver those insights to users through platform-level frameworks and integrations with Apple Intelligence. You'll work closely with CoreLocation, CoreMotion, Audio Understanding, and AIML colleagues to tackle open-ended, research-like problems — prototyping novel sensing capabilities and bringing them to production on Apple platforms.

Description

This role is for the platform-level work: daemons, frameworks, and ML inference pipelines beneath the UI layer, continuously interpreting the environment efficiently, privately, and deeply integrated with the OS. You will collaborate with ML researchers to translate models into production systems. Problems span low-level signal capture to high-level context modeling to APIs surfacing capabilities to developers and features across Apple platforms.

Minimum Qualifications

• BS/MS in Computer Science, Electrical Engineering, or related field
• 6+ years experience on client-side / platform-level development on Apple platforms
• Swift and Objective-C; production-quality frameworks, daemons, or system APIs
• Systems expertise: concurrency, multithreading, memory management, performance tuning
• CoreML or equivalent on-device ML inference experience
• Strong communication; operates well in ambiguity

Preferred Qualifications

• Real-time audio processing (capture pipelines, buffering, signal handling on iOS/macOS)
• REST API / cloud service integration
• Audio feature extraction or classification pipelines for on-device ML
• Experience training/fine-tuning ML models or collaborating with ML researchers to productionize them
• Deep OS stack debugging (frameworks, daemons, services)
• Energy efficiency and resource constraint optimization on mobile
• Privacy-preserving sensing (on-device inference, data minimization)
• CloudKit or distributed data sync
• Public or internal API/SDK design and delivery
• Mentorship and technical direction track record