Senior Machine Learning Engineer - System Experience Personalization

Our team is looking for you to help make iOS more intelligent, proactive and personal. Our team is part the core iOS experience, using privacy preserving on-device intelligence to drive new experiences that touch the lives of millions of Apple customers every day. \\n\\nWe are responsible for personalizing core system experiences, such as helping you manage and summarize notifications, get the most relevant widgets in smart stacks, as well as predicting what apps you will launch next. This is just the start of making iOS more intelligent and personal. In our team you will bring expertise in software engineering to create experiences that surprise and delight our customers every day!

You will work closely with talented Software and ML engineers on our team, and across Apple to design, architect and implement new experiences across iOS and all Apple platforms.\n\nAs we build the future of iOS, you will be responsible for driving the development of the machine learning models to power them. You will provide technical leadership across a wide variety of products and features, we will look to you to create innovative data and machine learning solutions. The work requires delivering high quality features while adhering to device power and performance constraints!\n\nYou will work closely with other talented engineers on our team and cross functional partners to design, implement and scale machine learning solutions to deliver new experiences across iOS and other platforms within Apple. \n\nWe are passionate about user experience and privacy. Our mission is to craft user experiences which leverage the power of machine learning and on-device intelligence to preserve our customers privacy. You will be a key addition to the team helping to build state-of-the art intelligence for millions of customers...

M.S. or PhD in Machine Learning, Computer Science or related field.\n5+ Years of proven experience building machine learning systems\nComprehensive understanding of machine learning algorithms, deep learning architectures, supervised, unsupervised and reinforcement learning modeling techniques, and their performance attributes.

Experience in resource constrained computing (embedded systems or mobile development)\nStrong foundation in Computer Science fundamentals and Software engineering best practices\nProficiency with machine learning libraries such as TensorFlow, Scikit-learn, PyTorch, or similar frameworks\nExperience working with large scale and real world datasets for classification, regression, ranking, or recommendation problems