PhD Research Intern - Experience Delivery (Summer '26)

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. 

 

What is the Research Internship Program?  

As a Research Intern at Dolby, you will have the opportunity to define and lead your own pioneering research project that connects big-picture change with the meticulous detail of technological innovation. Our intern research projects are driven by a greater purpose: enhancing the human experience. 

 

This opportunity will be based out of our research offices in San Francisco, Sunnyvale, or Atlanta, depending on the role. 

 

What are we looking for in candidates? 

We are seeking current PhD students with a diverse range of backgrounds and experiences. To be eligible, you should have completed at least one year of your doctoral program. Along with solid technical skills, candidates should demonstrate problem-solving and analytical abilities, good communication and collaboration skills, a curiosity for how and why things work as they do, and a passion for audio, video, movies, music, or game technology. You have a desire to bring in new ideas and are open to learning from others. 

 

Summary of the Position: 

We are a key research team within Dolby’s Advanced Technology Group, focused on advancing both cloud and network-delivered media experiences. We combine modern networking, distributed systems, and big data platforms with multimodal foundational AI models (audio, video, language) to deliver intelligent, reliable, and scalable experiences for the world’s most influential media service providers. We are looking for candidates with an interest in one or more of the following areas: 

 

Data Platforms: 

• Investigating distributed compute architectures for data, media processing and distribution maintaining high performance at lowest cost. 

• Developing big data management approaches and data and AI training platform architectures for heterogenous data domain spanning audio, video, text, tabular and time series data 

• Creating novel data-driven algorithms to improve the collection, profiling, cleaning, selection, and analysis of datasets to improve performance of AI/ML model training dynamics. 

 

Foundational AI Models: 

• Training, fine‑tuning, and alignment of audio, vision, language, and multimodal foundation models for media understanding, enhancement, personalization, and interaction. 

• Generative AI for media creation and enhancement (e.g., speech/music synthesis, source separation, bandwidth-aware super‑resolution/upscaling, captioning, description, translation, and dialog systems). 

• Self‑supervised and contrastive learning for robust cross‑modal representations (audio‑video‑text) and downstream retrieval/understanding tasks. 

• Evaluation of AI models for media tasks (objective metrics and perceptual studies), robustness to distribution shift, and trustworthy AI (explainability, uncertainty). 

 

Networks Optimization: 

• Digital communication and/or information theory targeted at improving the efficiency, performance, and reliability of data / media networks. 

• Identification, modeling, and control of non-linear dynamical systems using machine learning.  

• Investigating network, network virtualization and cloud compute topologies including performance modeling and related utility optimizations