Here we have exciting opportunities to work with SoftRobot. During the internship, you will be supervised by one of our machine learning engineers and work with state-of-the-art tools in NLP and CV. This is a great way to see how AI is used on a practical level to provide insights to end-users. With us, you will collaborate as a team, contribute to the development discussion, and peer into the DevOps infrastructure.
We are looking for passionate and enthusiastic students who are constantly learning in the fields of computer science and AI. We encourage curiosity and flexibility with where you take your ideas and how you transform them into something valuable. We require that our interns have an understanding of Python. Some knowledge of SQL and the Python frameworks/libraries within the scope of AI (e.g. Scikit-learn, Pytorch, Keras, Tensorflow, Huggingface) is a plus.
This research aims to explore the intricacies of prompt engineering for Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) architecture. You'll investigate how prompts can be decomposed to target specific LLM responses, refine these prompts for better accuracy and relevance, and examine the difference between LLM response output and human intention. The overarching objective is to optimize prompt engineering, ensuring improved output quality in a RAG context. This will be research that will go towards a product that is intended for production.
We would like to investigate and fine-tune a pretrained model for one of two use cases:
We would like to investigate the feasibility of constructing synthetic financial documents with advanced language models (e.g. BERT, GANs, GPT) as valuable input for other services where data is scarce. This research could be applied to a few products at the company.
When you apply, we ask you to attach a resume and cover letter to contact@softrobot.io. Please include the thesis suggestion you choose in the cover letter and why it seems interesting to you. The application review process will be conducted in the late fall.