Python · TensorFlow · Kubernetes · Docker · Git
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 backend engineer, you'd play a critical role in our search architecture. We're pretty flexible on what projects people work on based on their skills and interests.
Search quality is bounded by what we understand about a page. Before anything can be retrieved, something has to work out what the page actually says. That means parsing it into the parts that are content and the parts that are furniture, classifying what kind of page it is and what it is about, telling whether the page is usable at all, extracting when it was published, judging how good it is and whether it can be trusted, and working out whether it says anything that a page we already have does not. All of this has to work on every page on the web, in every language, in every shape the web comes in.
Some of this is classic document understanding. Some of it is much more open. Credibility and misinformation, AI-generated and machine-spun content, and pages written to be