Search API for AI agents that need real-time web data, deep research, and structured content from one endpoint. Try our API for free. Backed by Lightspeed, Y Combinator and a16z.
About the role
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.
What they're looking for
- You have graduate-level ML experience (Master's degree or PhD with at least 2 years of relevant experience, or are an exceptionally strong undergrad)
- You can code up a transformer from scratch in PyTorch
- You like creating large-scale datasets and diving deeply into the data
- You care about the problem of finding high quality knowledge and recognize how important this is for the world
More about this role
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.
The ML organization sits at the heart of our mission. We train foundational models for search. Our goal is to build systems that can instantly filter the world's knowledge to exactly what you want, no matter how complex your query. Basically, put the web into an extremely powerful database.
We're looking for an ML Research Engineer to train embedding models for perfect search over the web. The role involves dreaming up novel transformer-based search architectures, creating datasets, creating evals, beating our internal SoTA, and repeat.
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