kapa.ai makes it easy for developer-facing companies to build AI support and onboarding bots for their users. Teams at +150 leading developer-facing companeis incl. OpenAI, Mixpanel, Mapbox, Docker, Next.js and Prisma use kapa to level up their developer experience and reduce support.

We enable companies to use their existing technical knowledge sources incl. docs, tutorials, chat logs, and GitHub issues to generate AI bots that answers developer questions automatically. More than 750k developers have access to kapa.ai via website widgets, Slack/Discord bots, API integrations, or via Zendesk.

See a few examples live here:

• Docker "Ask AI" docs widget for their website: docker.com/docs
• OpenAI Discord bot for +100k devs (#ask-ai channel): twitter.com/OpenAI
• Netlify "Ask Netlify" docs widget available to their +1 million users: twitter.com/Netlify
• Mixpanel "Slack bot" for their 7.5k community members (#ask-ai channel): mixpanel.slack.com

We’ve been fortunate to be funded by some of the greatest AI investors in Silicon Valley: Initialized Capital (Garry Tan, Alexis Ohanian), Y Combinator, Amjad Masad and Michele Catasta (Replit), and Douwe Kiela (RAG paper author and founder of Contextual AI), and other folks incl. angels at OpenAI.

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.

Django, NextJS, and lots of custom retrieval augmented generation pipelining.