Student Researcher Intern

To further support long-horizon and high-impact AI research, we are launching the Seed Edge Research Initiative. This initiative focuses on developing general intelligence models — models that possess human-like learning abilities, interaction capabilities, and tool-use proficiency. We encourage bold, long-term research with inherent uncertainty and cross-modal, interdisciplinary collaboration. Seed Edge provides a highly flexible research environment and long-cycle evaluation, enabling researchers to pursue transformative AI challenges. What You Will Work On By joining the Seed Edge initiative, you will collaborate with us to explore: 1. Explore novel intrinsic reward mechanisms to enable models to engage in autonomous learning and continual self-improvement; 2. Explore the development of long-term memory mechanisms to support the next generation of efficient reasoning models and long-sequence reasoning and modeling.; 3. Advance the boundaries of multimodal perception by developing scalable methods to extract knowledge from massive multimodal data with ultra-low signal-to-noise ratios, while building a solid foundation for multimodal fusion and unified modeling; 4. Study how models can use tools and develop visually grounded action capabilities, including new approaches to foundation modeling for full-modality agents and interactive learning in complex environments. We also welcome self-initiated research ideas — bring your vision and curiosity to help shape the future of general intelligence. As part of the application process, we encourage you to prepare a research presentation. We're looking forward to deep technical conversations with you. Minimum Qualifications 1. Currently pursuing a PhD degree, graduating in 2027 or later, in Computer Science, Artificial Intelligence, Automation, Mathematics, Physics, or related fields; 2. Solid research experience in one or more of the following areas: LLMs, computer vision, multimodal learning, AIGC (AI-Generated Content), or machine learning; 3. Strong curiosity and problem-solving skills, with the ability to independently explore solutions; 4. Strong communication and collaboration skills, with a deep passion for advancing fundamental technologies. Self-driven and open-minded in exploring unconventional ideas through iterative experimentation, aiming to push forward the boundaries of AI research. Preferred Qualifications 1. Solid understanding of core machine learning principles and algorithmic thinking; prior publications at top-tier conferences/journals (e.g., CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML, SIGGRAPH, SIGGRAPH Asia, etc.); 2. Proficient in C/C++ or Python, with strong programming skills. Recognition in competitions such as ACM/ICPC, NOI/IOI, TopCoder, or Kaggle is highly valued. 3. Experience leading impactful projects in any of the following domains: multimodal learning, LLMs, foundation models, world models, reinforcement learning, or rendering & generative models.