Who are we?
AwesomeQA is an AI chatbot used for support in online communities. After training on a company's resources, it automatically answers customers and solves their problems.
Currently, it is being used in the largest communities of the web3 space such as Scroll, AAVE, and Dune. However, our vision is not limited to any one industry, and we want to make our tool a well-rounded automated support solution for all kinds of online communities.
To fulfill this vision, we closely develop our product based on customer feedback, so that our product is an effective solution based on market needs and not just another generic chatbot flooding the market.
As RAG / LLM Engineer at our young startup, you will be able to contribute to the core of our product, the AI. Having worked on a RAG system before the launch of ChatGPT put us in a great position and we want to keep this head start by continuously redefining the state-of-the-art. We also encourage you to join user calls and participate in shaping of the product.
This is the perfect role for anyone who wants to gain experience in RAG / LLM development while working at an impactful and fast-growing startup.
Performance objectives
- Rapid Experiments to improve our RAG-pipeline, including retrieval improvements, reranking, prompt engineering, setting the right context, crafting the source dataset, and anything else that can help to improve our automated responses.
- Integrate newly explored approaches in our python services.
- Introduce reliable evaluation metrics for our product.
- Continuously monitor & explore the data by hand to spot problems and opportunities.
- Stay up-to-date on the latest industry trends for RAG, and combine them with your own ideas to develop new hypotheses to test.
- Work effectively with the team to ideate, plan, and address customer feedback to create a highly effective roadmap.
- Actively participate in user calls, offering technical insights and understanding user needs for product development.
- Continually innovate, suggesting improvements for processes, system structures, or technologies.
Tech stack
We always try to use the best tools available for the job.
Our current tech stack looks like this:
- Next.js + React + Typescript on the frontend
- Python + FastAPI backend
- sentence-transformers / pytorch + tfidf to power the AI
- PostgreSQL, Firestore (Managed by GCP)
- Kubernetes & Docker for all services (Frontend, Backend, Discord-bot, etc.)
What we promise
- We greatly value psychological safety and honest feedback. We always expect you to speak up and share your ideas about what we can improve within the company, and take these views to heart.
- We do routine 1:1's with your team lead, retrospectives, and insight hours to make sure we are constantly getting better at what we do all while fostering employee mental health and wellbeing.
- Personal development is something strongly encouraged by AwesomeQA, so there are no fixed budgets when it comes to things that will foster your personal growth (education, trainings, conferences etc.), just ask and we usually say yes!
- We're excited to build the operating system for communities - and we want you to be too. That's why all full-time employees receive strong and fair stock option packages. We also support you in finding your perfect set-up.
How to apply
If you want to join AwesomeQA to revolutionize community management and you consider yourself a fit for this position, please reach out to us via hi@awesomeqa.xyz
We are looking forward to getting to know you!