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Research Engineer, Content Understanding
Contract type
Ongoing
Work mode
Remote
Experience
Mid-level · 2+ years
Job description
A research engineer role at an applied AI lab focused on document understanding for a web-scale search engine, working on parsing, classification, quality and credibility modelling of web pages.
At a glance
- Work mode
- Remote
- Languages
- English
What you'll do
- Make parsing work on the pages where it currently does not, and prove the improvement rather than assert it
- Teach a model to judge page quality, and get everyone to agree on what quality means well enough to supervise it
- Work on credibility and misinformation as a modelling problem: what a page claims, whether it is a reliable source of it, and whether it was written for a reader or for a crawler
- Decide whether two documents are semantically the same or genuinely different, so we can deduplicate the web without collapsing pages that a user would want to see separately
- Build classification and extraction that is accurate at web scale and cheap enough to run on all of it
- Design the supervision for something nobody has labels for, and find out whether it is learnable at all
- Trace a bad search result back to the page-level prediction that caused it, and fix it at the source
What we're looking for
- Graduate-level ML experience (Master's or PhD with at least 2 years of relevant experience), or an exceptionally strong undergrad
- You can build a transformer from scratch in PyTorch, and you have trained models that then had to be cheap enough to run everywhere
- You like building large-scale datasets and living in the data
- You are comfortable with problems where the ground truth does not exist yet and defining it is part of the job
- You care about the problem of finding high quality knowledge
Apply
Direct apply at Exa
Opens the job posting at Exa
About Exa
An applied AI lab building a search engine unlike the world has ever seen
Exa is an applied AI lab building a search engine. 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.
Mission
Build perfect search over all the world's information, far beyond Google
Key facts
- Applied AI / search
- Powers search for Cursor, Cognition, HubSpot, and over 400,000 developers
- Raised $350m from Lightspeed, Benchmark, and a16z
Partners & customers
CursorCognitionHubSpotLightspeedBenchmarka16z