Who we areAt MediaKind, we’ve reshaped the future of streaming video.
As the world’s largest independent video technology company, and the leader in sports streaming, we power the most ambitious and demanding video experiences on the planet.
Our cloud-native, software-defined solutions help the world’s leading streamers and broadcasters build, deploy and scale premium video experiences. From live sport and entertainment to mission-critical broadcast, we help them deliver every moment to audiences, everywhere.
At MediaKind, innovation is driven by people. You'll join a global team of curious thinkers, problem-solvers, and builders who move quickly, collaborate openly, and are passionate about delivering technology that makes a real impact. We value ownership, practical thinking, and continuous learning, and we believe great ideas come from empowered teams.
If you're excited by solving complex challenges, working alongside talented colleagues around the world, and helping shape the future of video, we'd love to build what's next together.
About the Team
MediaKind provides cloud-native video delivery and streaming solutions to broadcasters and service providers worldwide. Our Applied Video Research team is a small, highly focused group that turns advances in video compression, perceptual quality and machine learning into practical improvements for MediaKind products, such as encoding efficiency, quality monitoring and viewer experience.
About the Internship
As a Video Research Intern, you will actively support our applied video research roadmap. You will work under the close supervision of senior team members, who guide your scope, methods and reviews. You will own a well-defined piece of work, from state-of-the-art review through experimentation to a clear, shareable result. This is a Master's-level internship for a duration of 6 months (ideally starting in January / February 2027) and based in Rennes, France on a hybrid working mode.
Research Topics
Your project will be defined with you, according to your background and interests, around one or more of these areas (you are not expected to know them all in advance):
End-to-end learned compression: Survey the state of the art in neural video coding and assess practicality and feasibility against standard codecs (e.g. AVC, HEVC, AV1, VVC) under realistic deployment conditions, covering coding efficiency, complexity, latency and deployability.
Deep-learned perceptual quality metrics: Survey and benchmark the state of the art in full-reference video quality metrics (e.g. PSNR, SSIM, VMAF and learned metrics), for customer QoE monitoring and encoder tuning. Analyze their limits (e.g. alignment with human perception, local degradations hidden by global averaging, computational cost) and explore perception-aligned, local-aware and low-complexity improvements.
Perceptual and saliency maps: Review existing approaches (e.g. visual saliency and semantic analysis of perceptually sensitive regions such as faces, text and moving objects) and help design maps that can drive objective metrics or codec features, such as adaptive quantization and bit allocation.
Neural pre-/post-processing: Review and optimize neural processing stages around standard codecs (e.g. resampling, denoising, enhancement) and evaluate their impact on rate-distortion and perceived quality.
What You’ll Do
Run structured literature and state-of-the-art reviews and summarize findings for the team.
Set up and run experiments with open-source or proprietary codecs, models and datasets, depending on the topic.
Build and maintain reproducible evaluation, benchmarking and experimentation tools.
Investigate, develop and test proofs of concept (PoCs) with guidance from senior researchers.
Analyze results and document them in technical reports and team presentations, sharing knowledge across the team.
Contribute, where appropriate, to code deliveries, publications or patent disclosures.
Qualifications
Eligibility
Enrolled in a…