kapa.ai makes it easy for technical companies to build AI support and onboarding bots for their users. Teams at +150 leading startups and enterprises 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.

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 software engineer you will work across the stack on the kapa systems that answer thousands of developer questions a day. 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 research engineers.
• Drive forward the infrastructure that powers the kapa RAG engine.
• Experiment with new features in the kapa analytics platform.
• Work on the integrations which are used to deploy kapa for our customers.

You may be a good fit if you have:

• A degree in computer science, machine learning, mathematics, statistics or a related field.
• 3+ years of software engineering experience working on complex systems.
• An affinity for machine learning, deep learning (including LLMs) and natural language processing.
• Prior experience working on information retrieval systems.
• The ability to work effectively in a fast in a environment where things are sometimes loosely defined.

* 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.