Research Scientist (Multimodal Foundation Model) - 2026 Start (PhD)

Responsibilities

Established in 2023, the ByteDance Seed team is dedicated to pioneering new paths toward artificial general intelligence. We aspire to advance the frontier of intelligence to drive progress for both technology and society

With a long-term vision for the AI sector, the Seed team's research spans MLLM, GenMedia, AI for Science, and Robotics. We maintain a global presence with laboratories and career opportunities across China, Singapore, and the United States. To date, we have launched industry-leading general foundation models and cutting-edge multimodal capabilities. Our technology powers over 50 application scenarios — including Doubao, Jimeng, TRAE, Dola and Dreamnia — and serves enterprise customers through Volcano Engine and BytePlus. Third-party data shows that the Doubao App ranks first in user volume in the Chinese market, while Doubao foundation models lead the industry in average daily token consumption.

About the team

Welcome to the Vision-Research team, where we lead the way in developing foundational models for multi-modal visual understanding and generation. Our mission is to solve the challenge of visual intelligence in AI. We conduct cutting-edge research on areas such as vision and language, large-scale vision models, and generative foundation models. Comprising experienced research scientists and engineers, our team is dedicated to pushing the boundaries of foundation model research and implementing our innovations across diverse application scenarios. We foster a feedback-driven environment to continuously enhance our foundation technologies. Come join us in shaping the future of AI and transforming the product experience for users worldwide.

We are looking for talented individuals to join us in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with ByteDance.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities

- Conduct research on computer vision, deep learning and AI, addressing a wide range of challenges in deep learning, computer vision, AIGC, graphics, large multi-modality models, diffusion models, video generation, 3D generation, video understanding, self-supervised learning, and autoregressive models.

- Explore the application of large-scale/super-large-scale visual foundation models, and contribute to the development of new technologies and products leveraging artificial intelligence.

Qualifications

Minimum Qualifications

- Hold a Ph.D. degree in computer science, electrical engineering, statistics, applied mathematics, data science or related disciplines.

- Possess research and practical experience in one or more areas of computer vision, encompassing multimodal generation (e.g., text-to-image, image, video, 3D generation and editing), diffusion models, GANs, transformers for generation tasks, vision-language models, large-scale training and RLHF.

- Proven track record of high-impact research.

- Collaborate effectively with team members.

- Ability to work independently.

Preferred Qualifications

- Demonstrate impactful publications in leading AI conferences (e.g., CVPR, ECCV, ICCV, NeurIPS, ICLR, SIGGRAPH, SIGGRAPH Asia) and journals (e.g., TPAMI, JMLR).

- Achievement as a winner in international academic competitions.

- Proficiency in one of the differentiable programming frameworks such as PyTorch, TensorFlow, JAX, etc.

- Possess coding skills in C/C++ and Python.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://jobs.bytedance.com/en/legal/privacy.

If you have any questions, please reach out to us at [email protected]