Machine Learning Engineer, Computer Vision

Machine Learning, Computer Vision, Machine Learning, Computer Vision, Computer Science, Memory Hardware, Deep Learning, Apple, Data Analysis, Modeling Languages, We are looking for a Machine Learning Engineer with strong foundations in computer vision and machine learning, and a practical mindset: you care about making models work on real data, in real products. Our team builds foundational perception technologies. These models are built once and reused by many experiences, across iPhone, iPad, Mac, HomeView and more. They already power Apple Intelligence features such as Visual Intelligence and Clean Up in Photos. Our work spans a wide range of models, from lightweight, task-specific vision models to general-purpose vision-language models. In this role, you will build computer vision models and VLMs that help Apple devices recognize and understand the objects around the user. You will turn recent research into models that ship on millions of devices. Design, train and improve computer vision and VLM models for object understanding and recognition. Make models robust to real-world conditions and across devices and fix failure modes on real data Define evaluations that reflect product quality, and use them to drive model decisions. Design models for on-device constraints (memory, latency, power).Ph.D. or Masters in Computer Science, Machine Learning, Computer Vision or a related field, or equivalent industry experience. Strong foundations in computer vision and deep learning. Track record of making models work in practice: end-to-end projects covering data, training and evaluation on real-world visual data. Proficiency in Python and PyTorch or an equivalent deep learning framework. Proficiency in using generative coding tools Rigorous experimentation: clear baselines, ablations and meaningful evaluation sets.Publications in top computer vision or machine learning venues (CVPR, ICCV, ECCV, NeurIPS, ICLR), ideally with released code or work transferred to a product. Experience in representation learning, such as self-supervised learning, metric learning, retrieval or recognition. Experience applying machine learning in an industry or product setting. Experience with knowledge distillation or model compression. Familiarity with vision-language models.