Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z.
Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you.
As a Software Engineer on Knowledge Systems, you’ll help build systems that understand what is true about the world. You’ll work on extracting, connecting, retrieving, and reasoning over knowledge from the web and beyond so AI agents can answer questions with unprecedented precision and completeness.
Desired Experience
You have experience building and maintaining complex distributed backend systems
You can build data pipelines that process petabytes of documents, metadata, and unstructured data
You’re comfortable optimizing systems for correctness, reliability, and scale
You care deeply about data quality and finding high-quality information
Plus: experience building systems involving graph traversal, entity resolution, data quality, or truth discovery
Example Projects
Build a system that can answer every factual question about every person in the world
Design a new query language for searching, traversing, and reasoning over billions of facts
Skills Required
- Experience building and maintaining complex distributed backend systems
- Experience building data pipelines that process petabytes of documents, metadata, and unstructured data
- Experience optimizing systems for correctness, reliability, and scale
- Strong focus on data quality and finding high-quality information
- Experience with graph traversal, entity resolution, data quality systems, or truth discovery
What We Do
Exa was built with a simple goal — to organize all knowledge. After several years of heads-down research, we developed novel representation learning techniques and crawling infrastructure so that LLMs can intelligently find relevant information.
