About this role
Summary: Develop and deploy novel deep learning technologies for Siri, focusing on efficient machine learning for speech and multi-modal modeling. The role involves advancing state-of-the-art models for natural language understanding and integrating them into production environments to improve user experience across Apple's OS.
Join the team redefining what a deeply personal and integrated assistant can be. As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.
This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.
Description
On the Siri team, you will work alongside a fast-growing team of world-class engineers and scientists to tackle core problems in efficient machine learning for effective dialog systems and foundation models—ranging from natural language understanding and multi-turn context tracking, to the integration of speech, text, and other modalities.
Responsibilities
You will develop and deploy novel deep learning technologies that make Siri more intelligent, natural, and useful. You will see your ideas not only published in papers, but also improve the experience of millions of users.
As a researcher on our team, youʼll help us advance the state of the art in efficient machine learning for speech and multi-modal modeling, with a strong focus on running advanced models efficiently on server and devices, minimizing latency, preserving privacy and saving energy and bringing your innovations into production. Your ideas will directly impact the daily lives of billions of users through Siri.
Minimum qualifications
Demonstrated expertise in efficient deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying efficient deep learning techniques to products
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
PhD in Mathematics or Computer Science, or other technical field, or equivalent industry experience
Preferred qualifications
Strong expertise in efficient machine learning, model compression and algorithm optimization techniques
A track record in software design, coding and parallel computing
Experience with large scale machine learning training/evaluation
On-device intelligence and learning with strong privacy protections
Ability to work in a collaborative environment