1.  Engage in the research and development of large-scale video world models, including the design and construction of training datasets, foundational model algorithm design, optimization related to pre-training, SFT, and RL, model capability evaluation, and exploration of downstream application scenarios.

2.  Analyze R&D challenges scientifically, identify performance bottlenecks, and develop solutions based on first principles to accelerate the development and iteration of world models, ensuring competitiveness and leadership.

3.  Explore diverse paradigms for world model implementation, research next-generation model architectures, and push the boundaries of world model capabilities.

1.  Bachelor’s degree or higher (preferred) in Computer Science, Artificial Intelligence, Mathematics, or a related field.

2.  Solid foundation in deep learning algorithms and proven experience in large model R&D. Candidates with experience in Diffusion Models and Autoregressive Models, publications in top-tier conferences, or practical experience in text-to-image/text-to-video generation are preferred.

3.  Familiarity with implementation details of deep learning networks and operators, model tuning for training/inference, CPU/GPU acceleration, and distributed training/inference optimization. Hands-on experience is a plus.

4.  Participation in ACM/NOIP competitions is highly desirable.

5.  Strong learning, communication, and teamwork skills, coupled with a keen sense of curiosity.