AI/ML - Applied Research Scientist, Sensing Experiences

Seattle, Washington, United States
Machine Learning and AI


Role Number:200200197
Apple's central AI/ML org is looking for Applied Scientists who are passionate about using machine learning to build new user experiences. The team you will join is responsible for creating innovative experiences with machine learning and sensing. We are highly collaborative and partner with a variety of product teams across Apple including Watch, Siri, Accessibility, Home, and others and have shipped features like Raise to Speak on the Apple Watch. In this role, you will work with time-series data from multiple sensors, build data and machine learning pipelines, help integrate models on-device to power new experiences, and work with your team to iterate on the end user experience.

Key Qualifications

  • Experience applying signal processing and machine learning techniques to time-series sensor data (motion/IMU, audio, wireless technologies, optical, electrical and others)
  • Strong software development skills, with proficiency in Python
  • Creative, collaborative, & product focused


Our team believes in impacting people’s lives and the world directly. We do this by creating innovative intelligent experiences powered by machine learning and AI. We work across Apple on a huge variety of high impact product-focused inititatives. We prototype new experiences, develop and ship products, and we publish our work. We are creative, multi-disciplinary, and collaborative. Come join us to change the future.

Education & Experience

PhD in Machine learning, Computer Science, HCI, EE, Statistics, Physics, or related field with 3+ years of industry experience or MS in related field with 3-5 years industry experience

Additional Requirements

  • * Experience applying deep learning and sequence-based models (LSTMs, HMMs)
  • * Relevant domain experience in learning to detect gestures, activities, intent, or context on wearable or IoT devices.
  • * Ability to explain and present analyses and machine learning concepts to a broad technical audience