Research Engineer, Applied AI
Pubblicato il 07-09-2026 - kapa.ai in Italia
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 ( ) for a live example of what Kapa is (look for the “Ask AI” button).
Si ricordi di controllare il suo CV prima di candidarsi. Inoltre, si assicuri di leggere tutti i requisiti relativi a questo ruolo.
The Following Challenges Should Excite You
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- 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
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- 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
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- 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 , please reach out.
Location
Remote within Europe. We’re a distributed team and welcome applicants based an