Apple - Cupertino, CA
posted 3 months ago
As part of Apple's AI and Machine Learning organization, the Data and Machine Learning Innovation (DMLI) team is seeking a passionate Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges. This role involves collaborating closely with a multidisciplinary team of machine learning researchers, engineers, and data scientists to spearhead groundbreaking research initiatives and develop transformative products designed to create a significant impact for billions of users worldwide. In this position, you will be entrusted with the critical role of innovating and applying state-of-the-art research in machine learning to tackle sophisticated data problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will actively participate in the data-model co-design and co-development practice, which includes designing and developing a comprehensive data generation and curation framework for foundation models at Apple. Additionally, you will create robust model evaluation pipelines that are integral to the continuous improvement and assessment of foundation models. Your responsibilities will also include an in-depth analysis of multi-modal data to underscore its influence on model performance. You will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. The work may span a variety of applications, including improving current products and future hardware platforms with ML data, designing and implementing semi-supervised and self-supervised representation learning techniques, developing on-device intelligence with strong privacy protections, employing data selection techniques for diverse data types, and uncovering patterns in data to set performance targets and leverage modern statistical and ML-based methods to model data distributions.