Research Scientist Graduate (Seed Model - Multimodal Interaction & World Model) - 2027 Start (PhD)
About the role
The Seed Multimodal Interaction and World Model team is dedicated to developing models that have human-level multimodal understanding and interaction capabilities. The team is working to advance the exploration and development of multimodal assistant products. 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. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Responsibilities
- Develop multimodal foundation models integrating vision, language, audio, and environment signals.
- Design and optimize world models for reasoning, planning, and interaction.
- Build training pipelines including data curation, alignment, and reinforcement learning.
- Improve agent capabilities such as perception, memory, decision-making, and tool use.
- Explore next-generation interaction paradigms between humans and intelligent systems.
Requirements
- Successful candidates must be able to commit to an onboarding date by the end of the year.
Qualifications
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or a related discipline.
- Excellent coding ability, data structures, and fundamental algorithm skills, proficient in C/C++ or Python, etc.
- Experience in multimodal learning, reinforcement learning, or agent systems.
- Familiarity with large-scale model training or simulation environments.
Preferred Qualifications:
- Strong research track record in relevant areas.
- Strong problem-solving and collaboration skills.