Develop advanced deep learning models, focusing on handwritten text recognition, Chinese character error detection and semantic analysis, and improving system overall accuracy and computational efficiency.
Apply computer vision technology to process diverse handwritten inputs, model and optimize for complex scenarios such as similar characters, variant characters, and messy handwriting.
Design and improve the "stroke contrast" model architecture, enhance the ability to extract structural features of Chinese characters, and achieve high-precision similar character recognition.
Participate in the deployment, fine-tuning, and performance optimization of large language models (LLM), ensuring model stability, interpretability, and response speed in real-world applications.
Work closely with the engineering team to integrate AI models into the product production environment, and promote technical achievements to be applied in educational technology or language learning scenarios.
Based on user feedback and actual usage data, continuously iterate model performance, perform error analysis and system improvement.
Bachelor's degree or above in Computer Science, Artificial Intelligence, Data Science, or related fields.
In-depth understanding of deep learning and computer vision technology, and experience in handwritten recognition or natural language processing.
Familiar with PyTorch or TensorFlow, etc., deep learning frameworks, and have strong model development and debugging abilities.
Have experience in deploying and fine-tuning language learning models (LLM) and understanding optimization and evaluation methods.
Applicants with an interest in educational technology or language learning applications will be given priority.
Have problem-solving skills and be able to effectively communicate technical details and research results in a team environment.