Job Description

You will push the boundaries of the state-of-the-art in audio and media technologies. The ideal candidate would have a strong background in deep learning, both in terms of conceptual understanding, as well as practical experience. A core aspect of this role involves being able to keep up to date with the literature, implement, and innovate with the bleeding edge in generative models, self-supervised learning, and multi-modal learning. With the explosion of large language models and natural language processing, you will partner closely with Dolby s worldwide AI research staff, which actively pursues the integration of such models into audio and media experiences. You will be able to hit the ground running, innovate, and contribute to such projects. Consequently, experience with language models, question answering, vision-language models, captioning, etc. would be highly beneficial. 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. Main responsibilities Partner closely with other domain experts to refine and execute Dolby s technical strategy in artificial intelligence and machine learning. Use deep learning to create new solutions (including foundation models) and enhance existing applications. Push the state-of-the-art and develop intellectual property. Transfer technology to product groups and draft patent applications. Advise internal leaders on recent deep learning advancements in the industry and academia to further influence research direction and business decisions. Requirements Ph.D. in Computer Science or similar field. A strong background in deep learning, both in terms of conceptual understanding, as well as practical experience. Strong knowledge and interest in audio processing Knowledge in video, or text processing is desirable. Strong publication record, with publications in major machine learning conferences (e.g. NeurIPS, ICLR, ICML). Publications in top domain-specific conferences is desirable (e.g., ACL, CVPR, ICASSP). 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.