Applied Science Intern, PhD

Roku
Cambridge, United KingdomPosted 24 February 2026

Job Description

Teamwork makes the stream work. Roku is changing how the world watches TV Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers. From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines. About the team Roku continues a long-term trajectory of supporting summer internship roles within the organization, to provide applicants with experience of our enthusiasm, expertise and company culture. The Advanced Development, AI @ Roku (Roku’s internal AI productivity suite), Content, and Edge AI team within the Engineering organization has been granted 4 summer internships in 2026. These internships are only available to postgraduate students who are currently studying for a PhD or have recently successfully completed a PhD. About the role The purpose of the internship is to involve each student in the work of the AI @ Roku, Content, and Edge AI team. Each student will be assigned a supervisor among the senior members of the team. This supervision aims to ensure that the intern finishes their contract with a clear view of what permanent employment within the AI @ Roku, Content, and Edge AI team would be like. All the intern projects will be clearly defined at the outset, easy to get started and challenging to complete, and relevant to the AI @ Roku, Content, and Edge AI team’s Q3 and Q4 2026 goals. Project outlines: Multi-modal AI: Building new experiences for TVs with Vision-Language Models, focusing on multi-modal understanding and natural language interaction. Developing multimedia content generation techniques Content understanding and analysis for episodic and cinematic videos Deploying and optimizing AI algorithms to the edge Using AI to improve image and video quality What you'll be doing Define, agree, and track the meaningful measures (KPIs) of success in the project. Collaborate effectively with other members of the project team to achieve goals. Contribute meaningfully to the implementation and delivery of the project. Identify and communicate key learnings within and across project teams. Actively partner with experts in the project field in Cambridge, and internationally. Conduct feasibility studies to determine if 'new-to-the-world' ideas are viable for long-term integration into the Roku OS. Present findings and prototypes to the broader engineering and product leadership to influence Roku’s long-term innovation roadmap. We’re excited if you have PhD in STEM discipline (current or recent) Strong problem-solving and analytical skills Excellent communication and presentation skills Proficient in data visualization and interpretation High-level proficiency in Python Background in mathematics, statistics, and CS Ability to design and implement machine learning models Familiarity with machine learning frameworks (PyTorch, Tensorflow) Understanding of machine learning and computer vision algorithms and techniques Experience with image processing pipelines, embedding spaces or modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, LangFuse) is a plus. Our Hybrid Work Approach Roku fosters an inclusive and collaborative environment where teams work in the office Monday through Thursday. Fridays are flexible for remote work except for employees whose roles are required to be in the office five days a week or employees who are in offices with a five day in office policy. Ben ... (truncated, view full listing at source)
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