PhD Research Intern - Experience Delivery (Summer '26)

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 using machine learning.  

• Development of machine learning-based techniques for QoS/QoE prediction, resource management (e.g., edge compute), traffic classification, and routing optimization in Software Defined Networks (SDN). 

• Investigating the use of wireless sensor networks and data to improve the consumer entertainment experience in dynamic settings.  

• Information-Centric Networking (ICN) including Named Data Network (NDN) architecture for large-scale “user controlled” decentralized content distribution. 

Requirements (one or more of the following): 

• Working towards a PhD in Computer Science, Electrical Engineering, Computer Engineering, Data Science, or a related field, with an interest in digital communications, data networking, distributed compute, and machine learning. 

• Proficient in programming, e.g., C/C++, Python, Matlab or similar research/development tools. Familiarity with using GPUs and enabling frameworks is a plus. 

• Proficient in the use of machine learning and data science to solve large scale systems control or big data management and analysis problems. 

• Design and development of proof-of-concept test platforms to evaluate and summarize research outcomes through data analysis, visualization, and written reports. 

Highly Desired Experience in one of the following: 

• For Systems and Networking Focus: 

• Demonstrated knowledge of information and/or digital communication theory with a focus on channel or network coding.   

• Knowledge or exposure to the design of media distribution using network coding techniques. 

• Comprehensive understanding and working knowledge of modern networking protocols and distributed systems. 

• Experience in the use and applications of modern cloud compute architectures and information distribution in large-scale networks including decentralized network architectures. e.g., IPFS, ICN/NDN, etc. 

• Experience in developing distributed algorithms targeting improved control and utility maximization in distributed settings. 

• Development of novel algorithms for advancing QoS, QoE and network-related optimization problems in multimedia settings.  

• Nice to Have. Knowledge of state-of-the-art OTT media delivery paradigms, relevant standards and methods practiced in the OTT media market today. 

• For Data Focus: 

• Demonstrative experience in big data management and/or big data analytics. 

• Experience in distributed systems, distributed database storage engines, query processing and query optimization. 

• Expertise in foundational data management problems including data quality, data profiling, data integration and schema matching. 

• For AI Focus: 

• Experience in training, fine‑tuning, or evaluating deep learning models (preferably multimodal or audio/vision/language foundation models). 

• Proficiency in using modern deep learning frameworks such as PyTorch or TensorFlow, including experience with GPU‑accelerated training on multi‑GPU clusters or cloud platforms 

• Publication in a major data communication, networking, database/distributed systems, AI/machine learning publication/journal or conference as appropriate i.e., ACM SIGCOMM, IEEE GLOBECOM, IEEE ICC, IEEE Communications Magazine, IEEE Network, ICDCS, ACM SIGMOD, VLDB, NeurIPS, ICLR, ICML, CVPR, ACL, ICASSP, etc. 

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 October 17, 2025. 

Eligibility: 

Currently a PhD student in Computer Science, Electrical Engineering, Computer Engineering, Data Science, or a related field. Must be available to work full-time Monday – Friday for 12 weeks between May/June 2026 – August/September 2026.  

The start dates for this internship are as follows (please note these dates are not flexible):  

• May 18, 2026 or  

• June 15, 2026