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Sr. Foundational Audio AI Researcher

LinkedIn Dolby Laboratories Atlanta, GA
Not Applicable Posted March 30, 2026 Job link
Responsibilities

On the AI/ML team at Dolby Laboratories, you’ll partner with domain experts to shape and execute Dolby’s technical strategy in artificial intelligence and machine learning, using deep learning to build new solutions and improve existing applications. You’ll push the state of the art to generate intellectual property, transfer technology to product groups, and draft patent applications. You will also advise internal leaders on the latest deep learning advances in industry and academia to guide research direction and business decisions.

Commitments

Dolby will consider qualified applicants with criminal histories in accordance with San Francisco Police Code Article 49 and Administrative Code Article 12.

Not Met Priorities
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Requirements
  • A strong background in deep learning, both in terms of conceptual understanding, as well as practical experience.
  • Strong publication record, with publications in major machine learning conferences (e.g.
  • NeurIPS, ICLR, ICML).
  • Good knowledge about current machine learning literature.
  • Highly skilled in Python and one or more popular deep learning frameworks (TensorFlow or PyTorch).
  • Ability to envision new technologies and turn them into innovative products.
  • Good communication and collaboration skills.
  • Consequently, knowledge or experience in any/all of the following are helpful:
  • Diffusion, autoregressive, or other generative models.
  • Self-supervised, contrastive learning, auto-encoders.
  • Audio, image, or text applications – Source separation, text-to-speech, music synthesis, image segmentation, image captioning, question answering, language models, etc.
Preferred Skills
  • Consequently, experience with audio models, language models, question answering, vision-language models, captioning, etc. would be highly beneficial.
  • Knowledge in audio, video, or text processing is desirable.
  • NeurIPS, ICLR, ICML).
  • Publications in top domain-specific conferences is desirable (e.g., ACL, CVPR, ICASSP).
  • Good knowledge about current machine learning literature.
  • Consequently, knowledge or experience in any/all of the following are helpful:
  • Diffusion, autoregressive, or other generative models.
  • Self-supervised, contrastive learning, auto-encoders.
  • Audio, image, or text applications – Source separation, text-to-speech, music synthesis, image segmentation, image captioning, question answering, language models, etc.
Education
  • (Not required) – Ph.D. in Computer Science or similar field.