Research Engineer, Applied AI at kapa.ai (S23) $100K - $150K • 0.10% - 0.30% The fastest way to build AI assistants on technical content GB / DE / FR / NO / DK / SE / FI / PT / ES / BE / NL / IT / CH / AT / CZ / PL / EE / LV / LT / SK / HU / SI / HR / RU / UA / Remote (GB; DE; FR; NO; DK; SE; FI; PT; ES; BE; NL; IT; CH; AT; CZ; PL; EE; LV; LT; SK; HU; SI; HR; RU; UA) Full-time US citizenship/visa not required 3+ years About kapa.ai We make it easy for technical companies to build AI assistants. Companies like Docker, Grafana and Mixpanel deploy kapa in the following ways: As chat interface on their public documentation to answer developer questions. As first line of defense on their support forms to reduce tickets. As internal assistant for their GTM teams to navigate their own complex product. We leverage companies existing technical knowledge sources including documentation, tutorials, forum posts, Slack channels, GitHub issues and many more to generate AI assistants that can handle complicated technical questions. More than 200 companies use kapa and we have answered more than 10 million questions to date. About the role Kapa makes technical knowledge instantly accessible through AI assistants. As a research engineer you will work on improving Kapa’s ability to answer harder and harder technical questions. Check out Docker’s documentation (https://docs.docker.com) for a live example of what Kapa is (look for the “Ask AI” button). The following challenges should excite you: Evaluating a RAG system in production without labelled data. Creating your own benchmark from scratch. Building an agentic retrieval system that can judge when to be fast and when to take more time. Fine tuning embeddings or rerank models. To solve these challenges you will: Work directly with the founding team and our software engineers. Keep up with the latest developments in the space and see how they can be applied. Design and run experiments. 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. Technology Backend : Python, Django, Docker, Postgres, Weaviate, RabbitMQ Frontend: TypeScript, React, Next.js