Research Scientist Graduate; Foundation Model, Video Generation PhD

Position: Research Scientist Graduate (Foundation Model, Video Generation) - 2026 Start (PhD)
About the team Welcome to the Doubao Vision team, where we spearhead multi-modality foundation models on visual understanding and visual generation. Our mission is to solve the visual intelligence problem for AI. We conduct cutting-edge research on areas like vision and language, large vision models, and generative foundation models. The team is a mix of experienced research scientists and engineers, aiming to advance the research boundaries in foundation models and apply our technologies to our rich application scenarios, whereas a feedback loop is created to help further improve our foundation technologies.

Join us in shaping the future of AI technologies and revolutionizing our product experience for global users. 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 our Company. 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. Responsibilities - Conduct cutting-edge research and development in foundation model and multimodal machine learning, especially in the areas of generative AI (e.g. image, video, or 3d generation). The primary objective is to research cutting-edge video generation technology through innovation.

- Develop the foundation model to enhance the strategic advantages for Byte Dance products - Explore new downstream products with artificial intelligence technology at its core.

Minimum Qualifications:

- Individuals who are completing or have recently completed a PhD degree in Computer Science or related engineering field.

- Highly competent in algorithms and programming;
Strong coding skills in Python and PyTorch.

- Work and collaborate well with team members.

Preferred Qualifications:

- Candidates with publications in venues such as CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML, SIGGRAPH or Multimedia, etc.

- Hands-on coding experience in deep learning frameworks and large-scale training experience.

- Experience in large-scale image and video processing and curation, particularly when it involves extensive work with foundation models.