Responsibilities
1. Explore the application of AI technology in the field of Auto Dev and promote the implementation of cutting-edge technologies 2. Design and implement a large-scale code knowledge base based on AI and Vector DB to improve code understanding and knowledge utilization efficiency 3. Responsible for the research and application of (M) LLM, Agents and RAG technologies, build automated front-end and back-end engineering generation and maintenance capabilities, and conduct technical verification and continuous optimization 4. Explore the interaction mode of the next generation of Agent-based code generation products 5. Participate in the whole process of domain model, including but not limited to task construction, data collection, model training, evaluation, reasoning deployment.
Qualifications
1. Doctoral degree, major in computer science, software engineering, artificial intelligence, etc. 2. Have relevant experience in deep learning training frameworks such as TensorFlow/PyTorch, familiar with NLP, CV, RL related algorithm technology, and have excellent algorithm ability 3. Master the algorithm principle of (M)LLM, Fine-tuning, Prompt Engineering, (Multi-)Agent, vector database paradigm 4. Be enthusiastic about new technologies, keep track of the latest AI technologies in academia and industry, and have unique insights into Agent applications. Bonus points: 1. Candidates with fine-tuning/evaluation experience on (M)LLM are preferred 2. Candidates with hands-on experience on open source (Multi-)Agent frameworks, or experience in building Agent task flows from 0-1 are preferred 3. Candidates with PE/RAG development experience are preferred 4. Candidates with experience in publishing in top conferences/top journals in AI-related fields are preferred, and those with experience in publishing review articles are preferred.
