Research Scientist Graduate (Video Quality Analysis&Coding Strategy) - 2026 Start (PHD)
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About this role
Responsibilities
Our team designs and optimizes the next-generation end-to-end video system (for video production, processing, delivery, and consumption) to improve the quality of experience (QoE) for our billions of users. We are looking for strong video algorithm engineers from all areas of video understanding, video processing, video coding, video streaming, and video quality assessment, etc., who have a dedication to technical excellence and a passion to build large-scale and high-performing video platforms and services.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance
Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.
- 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 X-functional teams to integrate algorithms into production workflows and validate their impact through A/B testing.
Qualifications
Minimum Qualifications
- Final year Ph.D or recent Ph.D graduates in Computer Science, engineering or quantitative field
- 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, etc
- 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 etc
- Knowledge of common video processing algorithms, such as supperresolution, defusion model, etc.
- Great communication, eager to learn, and always passionate about turning cutting-edge technologies into real life use cases.