Research Scientist Graduate

About The Team

At Multimedia Lab, we push the boundaries of what’s possible in multimedia technology. Our mission is to pioneer cutting-edge research across image and video understanding, generation, processing, compression, and transmission—and transform these innovations into real-world products that delight hundreds of millions of users globally.

Topic Content: Multimodal Foundation Models for Intelligent Multimedia Processing

Explore next-generation multimedia technologies powered by multimodal foundation models, including perceptual quality modeling, generative enhancement, temporal video understanding, user-centric evaluation, and intelligent visual representation/compression, to advance video quality, efficiency, and user experience in future multimedia systems.

Key Responsibilities

• Design video analysis (ROI/SOD, content understanding, temporal grounding etc.) and quality assessment algorithms, and participate in database creation, algorithm design/development/optimization, etc.
• Participate in designing strategy and solution for E2E video quality optimization with a combination of video analysis, processing and encoding algorithms.
• Apply designed algorithms for VOD / Live streaming monitoring, data analysis, objective evaluation for algorithms etc.
• Collaborate with cross-functional teams to integrate algorithms into production workflows and validate their impact through A/B testing.

Qualifications

Minimum qualifications

• Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• In-depth knowledge of video analysis algorithms or subjective/objective video quality algorithms, and state-of-the-art technologies.
• Proficient in one of the following: C, C++, Python.

Preferred Qualifications

• Familiar with ML and image processing tools, including sklearn, opencv, ffmpeg.
• Familiar with deep learning frameworks (Tensorflow/Pytorch).
• Familiar with Transformer architectures and mainstream multi-modal large models (MLLMs), and hands-on implementation or research experience preferred.
• Familiar with Linux development environments, shell scripting, HDFS.
• Knowledge of common video processing algorithms, such as super-resolution, diffusion model.
• Great communication, eager to learn, and always passionate about turning cutting-edge technologies into real-life use cases.