AIML Software Engineer, -Device Audio Intelligence, Sensing & Connectivity
Listed on 2026-09-18
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
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 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 Core Location, Core Motion, Audio Understanding, and AIML colleagues to tackle open-ended, research-like problems — prototyping novel sensing capabilities and bringing them to production on Apple platforms.
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 QualificationsBS/MS in Computer Science, Electrical Engineering, or related field6+ years experience on client-side / platform-level development on Apple platforms
Swift and Objective-C; production-quality frameworks, daemons, or system APIsSystems expertise: concurrency, multithreading, memory management, performance tuning
CoreML or equivalent on-device ML inference experience
Strong communication; operates well in ambiguity
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 product ionize them
Deep OS stack debugging (frameworks, daemons, services)
Energy efficiency and resource constraint optimization on mobile
Privacy-preserving sensing (on-device inference, data minimization)
Cloud Kit or distributed data sync Public or internal API/SDK design and delivery
Mentorship and technical direction track record
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