Sr. Machine Learning Research Engineer, Siri Speech

Summary

Build efficient deep learning models for Siri's next-generation AI assistant, working on dialog systems, foundation models, natural language understanding, multi-turn context tracking, and multi-modal speech/text integration across iOS, iPadOS, macOS, watchOS, and visionOS.

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.

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.

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

See also

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