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.
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.
Minimum Qualifications:
Currently pursuing a PhD in computer science, mathematics, engineering, or a related field, with an expected graduation date in 2027 and the ability to commit to an onboarding date by the end of 2027.
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.
Numbers & Facts
Location
San Jose, CA
Skills
Algorithmsunmatched
C Programming Languageunmatched
C++ Programming Languageunmatched
Computer Scienceunmatched
Data Structuresunmatched
Mathematicsunmatched
Memory Hardwareunmatched
Multimodal Interactionunmatched
Onboardingunmatched
Problem Solving Skillsunmatched
Product Developmentunmatched
Python Programming/Scripting Languageunmatched
Reinforcement Learningunmatched
Scientific Researchunmatched
Simulationunmatched
Team Playerunmatched
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