Machine Learning Engineer

The Applied Sensing & Health team has built innovative ways for users to improve their health and fitness. When you exercise and move with your devices, it's the sensor fusion algorithms from the engineers and scientists on this team, that track human motion and provide interpretable insights. Join us to work with people who have expertise and passion to model human movement, ambient sensing, and more to have a positive impact in users' lives. As a member of our dynamic group, you will have the unique and rewarding opportunity to develop state of the art multimodal fusion of sensors, shape and contribute to the intelligence of upcoming products that will delight and inspire millions of Apple's customers every single day. The Applied Sensing & Health team delivers Health and Fitness features for Apple Watch, iPhones and other Apple products. We are looking for ML engineers who care deeply about their craft to join us. The roles and responsibilities include scoping, designing and implementing models for Health and Fitness algorithms, validating algorithms with synthetic data flows and real datasets with generative AI, and coordinating closely with multi-disciplinary teams across the company. 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. MS 3 + years experience in quantitative data science discipline (statistics/biostatistics, epidemiology, computer science). Strong background in developing machine learning and/or deep learning models, preferably with time series data. Strong proficiency in Python and ML frameworks e.g. PyTorch, TensorflowPh.D or 5+ years experience in quantitative data science discipline (statistics/biostatistics, epidemiology, computer science). You can form hypotheses, and can creatively apply different statistical approaches to the data in proving the hypotheses. You appreciate the computational and storage complexities that come with modeling using large datasets. You leverage distributed compute/storage models when the scale of data calls for it. You believe that the integrity of the tooling and pipelines are critical to coming up with high quality analyses, and are creative and disciplined about validation. You understand the role feedback plays in your growth, and how effective communication affords more feedback opportunities.