About the job
The Applied Sensing & Health team develops software that powers the next generation of fitness, safety, and health experiences. We transform complex, multi-modal sensor data from the iPhone, Apple Watch, and AirPods into meaningful and elegant insights about our users' health, wellbeing, and safety. By combining advanced machine learning—including the integration and tuning of foundation models—with domain knowledge and scientific research, we have delivered impactful features like Cardio Fitness, Journaling Suggestions, Fall and Crash Detection, and Walking Steadiness. As a dynamic and highly multi-disciplinary team working at the intersection of research and product development, you will have the opportunity to build the next generation of sensing-based features that will motivate, inform, and inspire millions of Apple's customers every single day.
Responsibilities
In this role, you will implement and ship interactive software features and algorithms that impact millions of users on a daily basis. You will be responsible for optimizing software implementations for power, memory, and performance, especially within the constraints of embedded, low-power systems. Operating at the intersection of software, data engineering, and machine learning, you will collaborate closely with scientists, engineers, QA, and project managers throughout the software lifecycle.
Qualifications
Minimum
Bachelor's in Computer Science, EECS, or equivalent experience.
Developed C/C++, ObjC or Swift code for a shipping product or a peer reviewed environment.
Background in developing, debugging and optimizing algorithms for low-power embedded systems
Preferred
Masters or PhD in Computer Science, EECS, machine-learning or equivalent experience.
2+ years of technical experience in an industry setting
Background in developing deep learning, foundation, and/or generative AI models for multiple data modalities (time series, images, language, etc.)
Experience collaborating in small teams
Excellent interpersonal skills and communication (written and verbal)