As a research engineer you will work on improving kapa’s ability to answer harder and harder technical questions. Check out Docker’s documentation for a live example of what kapa is.
In this role, you will:
- Work directly with the founding team and our software engineers.
- Work on and do research in state-of-the-art retrieval and search techniques.
- Work on and deploy machine learning models as part of RAG.
- Continuously improve our quality evaluation frameworks to enable robust iteration.
- Keep up with the latest developments in the space and see how they can be applied.
- Design and run experiments.
In addition to the founding team, you'll have support from a number of leading academics in the field that are all close advisors (incl. Douwe Kiela, author of the original RAG paper).
You may be a good fit if you have:
- A Master's/ PhD degree in Computer Science, Machine Learning, Mathematics, Statistics or a related field.
- A detailed understanding of machine learning, deep learning (including LLMs) and natural language processing.
- Hands-on experience in training, fine-tuning and deploying large language models.
- Have prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases.
- Significant experience building evaluation systems for LLMs or search.
- Familiarity with various information retrieval techniques, such as lexical search and dense vector search.
- The ability to work effectively in a fast in a environment where things are sometimes loosely defined.
- Want to learn more about machine learning research.
* This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to kapa.ai, please reach out.
kapa.ai
