Stack AI is a no-code drag-and-drop tool to quickly design, test, and deploy AI workflows that leverage Large Language Models (LLMs), such as ChatGPT, to automate any business process.

Our core value is to make it extremely easy to build arbitrarily complex AI pipelines using a visual interface that allows you to connect different data sources with different AI models.

Our customers use Stack AI to build applications such as:

• Chatbots and Assistants: AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs.
• Document Processing: apps to answer questions, summarize, and extract insights from any document, no matter how long.
• Answer Questions on Databases: connect GPT-like models to databases (such as Notion, Airtable, or Postgres) and ask questions about them.
• Content Creation: generate tags, summaries, and transfer styles or formats between documents and data sources.

About Stack AI

1. Vision: Democratize access to Large Language Models, so that anyone can build AI-powered applications that generate positive impact to the world.
2. Product: A user-friendly and intuitive No-Code platform integrating best AI models (OpenAI, Anthropic, Google, open source), most common data sources (OneDrive, Google Drive, Airtable, Notion) and SaaS (Zapier, HubSpot, etc.). We do the heavy-lifting for you, so you can focus on the product and business.
3. Traction: Launched 8 months ago; over 35,000 users and 300+ paying customers (including public companies and unicorns!).
4. Team: Founded by two MIT PhDs, backed by major investors such as Y Combinator and Gradient Ventures, we are now a four-engineer team.
5. Values: Default to action, radical transparency, boldness, and strong opinions loosely-held.

Responsibilities:

We need 10X engineers to make this happen! True wizards that can deliver on a daily basis in the high-paced environment.

In this role, you will...

• Enhance our retrieval-augmented generation (RAG) capabilities, ensuring our models effectively integrate and utilize diverse data sources.
• Work on embedding generation techniques, optimizing them for accuracy and efficiency.
• Keep abreast of the latest developments in AI models, their evolving capabilities, and the complexities of integration. Position yourself as the go-to expert within the team, guiding our end users in crafting their use cases with the most suitable models.
• Collaborate with backend and frontend teams to integrate AI models into products and services.
• We're all about our users - listen to their needs, help them, build strong relationships! Expect to dive into customer support now and then as everyone else in the team.

Required Skills and Qualifications:

You will be a great fit if you have...

• Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or related fields.
• Proven experience in machine learning and AI, with a focus on generative models.
• Knowledge of retrieval-augmented generation pipelines and their implementation.
• Strong proficiency in Python and popular machine learning frameworks like TensorFlow or PyTorch.
• Experience with embedding techniques and understanding of their applications in AI models.
• Excellent problem-solving skills and the ability to work in fast-paced environments.
• Strong communication and collaboration skills.

Preferred Qualifications:

You will be an exceptional fit if you also have...

• PhD in a relevant field.
• Published research or projects in the field of generative AI, embeddings, or retrieval-augmented generation.
• Experience with large-scale ML deployments in a production environment.
• Contribution to open-source AI/ML projects.
• In-depth knowledge of generative AI hardware, including an understanding of GPU architectures, requirements, and optimization techniques for AI model deployment.

Beyond these skills, what we value the most is your ability to remain curious and open to learning new skills. As a early-stage startup joiner, you may need to wear many hats.

Our tech stack includes:

• Frontend: Next.js + Tailwind (Typescript)
• Backend: FastAPI + Supabase (Python)
• Databases: PostgreSQL + MongoDB

And we have internally built a super easy-to-use Machine Learning framework tailored to using Large Language Models in a flow-like sequence (akin to Pytorch + Langchain if you are familiar with those). It allows you to seamlessly integrate new functionality into the code base and we are also discussing whether to open-source it since it feels like magic!