Senior Software ML Engineer - Watch Software

Our team is working toward a future where our devices are aware of us and our environment, they directly support our health and wellbeing, and they nudge us to be more thoughtful, present, and inspired human beings. We believe there is huge opportunity to improve lives and the world by understanding people, our activities, connections, and the environments we live in using sensing and machine learning on our devices. Our team works with cross-functional partners across Apple to create high-impact features and new ways to interact on Apple Watch, home products, and new hardware. We prototype new experiences, develop and ship products, and publish our work. We are a creative, multi-disciplinary, optimistic, and collaborative team. Come join us and build the future!

The team you will join is responsible for creating the technologies that power new, innovative product features for Apple Watch, like DoubleTap, AssistiveTouch, Handwashing, and Raise to Speak. We are highly collaborative and partner with a variety of research and product teams across Apple to explore novel experiences and ship features. We are looking for a versatile Machine Learning Software Engineer who is passionate about developing innovative, ML-driven product features that push the boundaries of sensing and human-computer interaction, and who can work across disciplines — from training advanced models to rapid on-device prototyping and full-scale productization.

Minimum Qualifications

• M.S. or Ph.D. in Computer Science, Machine Learning, or a related field.
• Proficiency with Swift or Objective-C and developing on Apple platforms.
• Experience using Python and deep learning frameworks such as PyTorch or TensorFlow for predictive modeling.
• Excellent communication and collaboration skills, with ability to work independently or in small teams.

Preferred Qualifications

• Experience optimizing and profiling applications for performance and power consumption.
• Ability to work within the full ML development cycle: data collection, model training and optimization, defining metrics, evaluation, performing failure analysis, and model deployment to resource constrained devices