苹果Computer Vision/Machine Learning Intern (Agentic AI)

The computer vision algorithm intern will work in a dynamic team as part of the Video Engineering org which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands. Keywords: Object detection and segmentation; Multiple sensor fusion; Activity Recognition; Video Caption

The computer vision algorithm intern will work in a dynamic team as part of the Video Engineering org which develops multi-modality based video quality assessment technologies in Apple Platform. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands. Keywords: Multi-Modal LLM; Video Quality Assessment; Post-training

• Collaborate with team members to understand failure analysis challenges and translate them into actionable technical solutions. • Develop and deploy AIML models and algorithms to diagnose and predict product failures. • Perform data analysis and visualization to extract actionable insights. • Test, validate, and optimize models to ensure high performance and reliability. • Document model development processes and findings, presenting results to stakeholders. • Stay informed about the latest advancements in AIML technologies and apply them to ongoing projects.

As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in ML to tackle complex data problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of a comprehensive data curation framework. You will also create robust model evaluation pipelines, integral to the continuous improvement and assessment of ML models. Additionally, your role will entail an in-depth analysis of collected data to underscore its influence on model performance. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. Your work may span a variety of topics, including but not limited to: * Designing and implementing semi-supervised, self-supervised representation learning techniques for maximizing the power of both limited labeled data and large-scale unlabeled data. * Developing evaluation protocols centered on the end-to-end user experience, with a focus on anticipating potential failure modes, edge cases, and anomalies. * Employing data selection techniques such as novelty detection, active learning, and core-set selection for diverse data types like images, 3D models, natural language, and audio. * Uncovering patterns in data, setting performance targets, and leveraging modern statistical and ML-based methods to model data distributions. This will aid in reducing redundancy and addressing out-of-distribution samples.