Research Scientist, Audio

DeepMindNew York, NY
8d$147,000 - $211,000

About The Position

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority. About Us Members of the team are a group of researchers with core contributions into the Gemini Audio pillar. Specifically, the team works on audio and audio-visual understanding and generation tasks using large language models. Research includes, but is not limited to, better acoustic representations and tokenizers, better generation modeling, and audio and audio-visual open-ended tasks such as dialog, TTS, question-answering and dubbing. The Role Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence. In this role, responsibilities will include making key contributions into the latest research developed in the Gemini audio pillar, such as:

Requirements

  • PhD in Computer Science, Computer Vision, Speech Processing, or Machine Learning related field.
  • Experience working with LLMs.
  • Audio or video understanding and/or generation experience.

Nice To Haves

  • A proven track record of research and publications in some of the following areas: audio generation, video generation, LLMs
  • A real passion for AI!

Responsibilities

  • Data: Unlocking new audio to X capabilities within the model, both in pre-training and post-training.
  • Models: Improving quality of models for understanding and generation. This includes research to improve our tokenizers, better techniques for generation quality, and looking at joint audio and visual representations.
  • Evals: Better evaluation methods (human, auto raters, automated metrics) to measure quality of open-ended tasks.
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