We are a group of engineers and researchers responsible for building foundation models at Apple. We build infrastructure, datasets, and models with fundamental general capabilities such as understanding and generation of text, images, speech, videos, and other modalities and apply these models to Apple products. We are looking for researchers who are passionate about developing algorithms, techniques, and systems that push the frontier of deep learning and delight millions of users with Apple products powered by foundation models.

Description

We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast-growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning, including natural language processing, multi-modal understanding, and combining learning with knowledge. Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see you ideas not only published in papers, but also improve the experience of billions of users.

Minimum Qualifications

Demonstrated expertise in deep learning with a publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying deep learning techniques to products Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow Ability to work in a collaborative environment PhD, or equivalent practical experience, in Computer Science, or related technical field

Preferred Qualifications

Web-scale information retrieval Human-like conversation agent Multi-modal perception for existing products and future hardware platforms On-device intelligence and learning with strong privacy protections

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AIML - Machine Learning Researcher, Foundation Models

Description

Minimum Qualifications

Preferred Qualifications

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