PhD Research Intern - Multimodal AI (Fall 2026)

Join the leader in entertainment innovation and help us design the future. The Advanced Technology Group (ATG) is the research division of the company. ATG’s mission is to look ahead, deliver insights, and innovate technological solutions that will fuel Dolby’s continued growth. As a valued member of the Dolby team, you’ll see and hear the results of your work everywhere, from movie theaters to smartphones. We continuously push the boundaries of audio, imaging, and cloud technology to create spectacular entertainment experiences. As a diverse and dynamic group, our ATG researchers work on cutting-edge projects related to computer science and electrical engineering for audio, video, and cloud technologies, exploring exciting domains such as AI/ML, algorithms, digital signal processing, audio processing, image processing, computer vision, AR/VR, data science & analytics, distributed systems, cloud, edge & mobile computing, computer networking, and IoT. The Multimodal Lab is looking for a talented, self-motivated PhD student to explore multimodal AI models for multimodal source separation, and spatial media content creation and generation. This is a research-focused role ideal for candidates passionate about pushing the boundaries of audio-visual AI at the intersection of deep learning, signal processing, and generative media. Responsibilities * Develop and apply multimodal AI architectures that integrate audio, visual, and/or language modalities for joint understanding and generation * Design, implement and train advanced multimodal AI models for spatial media content creation and generation, including multimodal source separation and localization * Prepare and curate high-quality datasets through data augmentation and synthetic data generation * Evaluate proposed models against state-of-the-art research benchmarks. * Prototype and validate the developed algorithms in realistic use cases * Present research findings and contribute to patent applications and scientific publications. Qualifications * Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field * Strong background in deep learning with proven ability of applying it to multimedia research challenges * Deep familiarity with leading AI model paradigms, including large language models (LLMs) and generative models (e.g., diffusion, VAE, GAN) * Proficiency in Python and at least one deep learning framework * Strong mathematical foundation * Excellent written and verbal communication skills We will review applications on a rolling basis. For the best chance to have your resume reviewed and considered, we recommend submitting your application by June 26, 2026. Eligibility Currently enrolled in PhD program. Recent grads who are within 6 months of graduation are also eligible to apply. Must be available to work full-time Monday – Friday for 12 weeks between September 2026 – December 2026. The start date for this internship is as follows (please note these dates are not flexible): * September 21, 2026 The San Francisco/Bay Area base hourly range for this internship position is $62/hr and can vary if outside of this location. Our hourly ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific hourly range and perks and benefits for your location during the hiring process. Dolby will consider qualified applicants with criminal histories in a manner consistent with the requirements of San Francisco Police Code, Article 49, and Administrative Code, Article 12 Equal Employment Opportunity: Dolby is proud to be an equal opportunity employer. Our success depends on the combined skills and talents of all our employees. We are committed to making employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, gender identity, national origin, religion, marital status, family status, medical condition, disability, military service, pregnancy, childbirth and related medical conditions or any other classification protected by federal, state, and local laws and ordinances.