Machine Learning Engineer, Sensing & Connectivity

The Motion & Interaction team has created intuitive experiences for our customers through motion sensing. When you simply raise your wrist, shake your head, or move your device to interact, it’s the work of engineers and scientists on this team. Our fingerprints can be found across core capabilities and experiences on iPhone, Watch, AirPods, Vision Pro, and other Apple products. We are a multidisciplinary team that operates at the intersection of algorithms, software, hardware, and design. We come from diverse backgrounds in signal processing, machine learning, software engineering, statistics, controls, firmware development, and more. As a member of our dynamic group, you will have a unique opportunity to work cross-functionally to develop products and features that impact the lives of millions of users worldwide a daily basis.

Description

We are seeking a talented, self-motivated machine learning engineer to build Apple’s next-generation features and experiences using multi-modal sensing. In this role, you will ideate, design, and implement models & algorithms, while optimizing for power, memory, and performance. You will be working on motion sensing-related features, including sensor fusion and interactive technologies.

Minimum Qualifications

MS, PhD, or 5+ years experience in machine learning, computer science, or related fields Professional experience creating and experimenting with machine learning models, preferably with time series data Strong proficiency in Python, machine learning tools and frameworks e.g. PyTorch, TensorFlow

Preferred Qualifications

Results oriented, with a proven ability to effectively prioritize and deliver tasks on schedule Excellent communication and collaboration skills Strong product sense, including the ability to balance technical feasibility with user experience Experience developing for embedded or real-time systems Experience leveraging distributed compute/storage models when the scale of data calls for it Experience designing and implementing interfaces between algorithms, software, and firmware Experience with multi-modal inputs and models, including IMU, images, video, and/or audio