Software Development Engineer, Test, Graphics, Games & ML

The On-Device Machine Learning team at Apple enables the Research to Production lifecycle of innovative machine learning models that power magical user experiences on Apple’s hardware and software platforms. Apple is the best place to do on-device machine learning, and this team sits at the heart of that subject area, collaborating with research, SW engineering, HW engineering, and products. Apple's On device Machine Learning Infrastructure and Quality team is seeking a highly motivated and detail oriented software engineer to drive innovations in quality for on device intelligence. The right candidate for this position is passionate about delivering the best possible experience for our users and is continuously looking for new ways to measure and improve the quality of our software stack and infrastructure. Additionally, having the ability to switch between designing creative product usage scenarios and immersive analysis of detailed feature design will be a critical skill to possess.

Description

The Software Development Engineer- Test will interact multi-functionally with many teams across Apple, impacting all levels of the Apple’s on device machine learning stack including hardware, drivers, frameworks and developer tools. In addition, you will develop and implement comprehensive manual /automated test plans and maintaining CI/CD presubmission pipelines. You will also be the voice of our customers, championing quality software development through each step of the development process and driving quality improvements throughout the organization.

Minimum Qualifications

BS, MS, degree or equivalent 2+ years of related experience in software quality engineering In depth knowledge of QA practices and fundamentals Strong Python programming skills A passion/interest for ML, particularly applied to on-device use cases. Good communication skills, including ability to communicate with multi-functional audiences.

Preferred Qualifications

Experience with Machine Learning, its common practical applications, and commonly used frameworks (e.g. Keras, PyTorch, Tensorflow, Scikit-learn)