IT Intern
2 days ago
Beijing, Beijing, China
Cummins Inc.
Full time
About the RoleCummins Inc. is seeking a highly motivated and detail-oriented IT Intern to join our team as a Large Language Model Specialist. In this role, you will have the opportunity to work with cutting-edge technology and contribute to the development of innovative solutions.
Key Responsibilities- Conduct research on existing large language models and their applications, assessing their potential value to our business.
- Assist in designing and implementing prototype systems based on LLMs to address specific corporate challenges or optimize existing processes.
- Participate in cross-functional team meetings to provide insights and suggestions on how to best utilize LLM technology.
- Document research findings, experimental results, and technical specifications for knowledge sharing within and outside the team.
- Support the team in overcoming technical challenges encountered during project management and execution.
- Currently pursuing an undergraduate or graduate degree in computer science, software engineering, data science/AI, or a related field.
- Solid understanding of large language models with practical experience using mainstream open-source models (e.g. Qianwen, GLM, LLMA).
- Proficient in Python programming and familiar with at least one deep learning framework (e.g. TensorFlow or PyTorch).
- Strong curiosity and self-motivation, capable of working efficiently in a fast-paced environment.
- Excellent communication skills, able to clearly articulate complex technical concepts.
- Familiarity with relevant technologies in large language models (LLMs): understanding the basic concepts and application frameworks of LLMs, such as Transformers, pre-trained models, prompt engineering, retrieval-augmented generation, fine-tuning, LangChain, etc.
- Experience winning awards or publishing papers in NLP or LLM-related competitions.
- Familiarity with tools such as Hugging Face Transformers.
- Awareness of the limitations and challenges of LLMs in real-world scenarios.