Applied Sensing & Health Software Engineer, Sensing & Connectivity

Applied Sensing & Health Software Engineer, Sensing & Connectivity

Join the Applied Sensing & Health team and build the software systems that will enable the next generation of fitness, safety, health experiences. We are a highly multi-disciplinary team and we tackle the most challenging problems in this space. As a member of our team, you will have the unique and rewarding opportunity to shape upcoming products that will motivate and inspire millions of Apple's customers every single day.

Responsibilities

• The roles and responsibilities include designing and implementing software for algorithms, optimizing implementations for power, memory and performance, and operating at the intersection of software and data engineering. You will work with scientists, engineers, QA, and project managers throughout the software lifecycle in successfully delivering best-in-class secure and scalable systems. Most importantly, you will help ship features that impact millions of users on a daily basis.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, EECS, or equivalent experience.
• Developed C/C++, ObjC or Swift code for a shipping product in a peer reviewed environment.
• Experience with software designs where logic/behaviors crosses thread, process, and machine boundaries.
• Implemented signals processing, statistical modeling, and/or machine learning algorithms to run on platforms with limited compute and memory.
• Employed usage of higher level languages (e.g. python, matlab) and posix utilities to expedite common computing tasks.

Preferred Qualifications

• Demonstrated ability to break down complex requirements into orthogonal and independent components for the purpose of building composable software.
• Experience developing highly scalable applications in a distributed compute/storage platform (e.g. Spark).
• Objective-C or Swift.
• Modern Object Oriented Programming idioms.
• Navigating and understanding large code bases.
• Deployment of performance machine learning models on portable computing devices.