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.
