Join Exa as a Research Engineer to define indexing signals and improve search quality in a cutting-edge AI environment.
Posted by employer 3 days ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
Skills & Technologies
What you'll build
- Own the signals behind indexing decisions
- Improve parsing on web pages
- Teach a model to judge page quality
- Work on credibility and misinformation
- Decide document semantic similarity
Must have
- Hands-on ML experience with classifiers and rankers
- Strong data instincts at web scale
- Ability to define what the right answer means
AI in the day-to-day
We build massive-scale ML systems to improve search over all the world's information.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the research engineer focuses on developing and refining the signals that determine which web pages are indexed, ensuring that search results are relevant and high-quality. The position requires hands-on experience with machine learning techniques, particularly in classifiers and rankers, as well as a strong understanding of data at scale. This role is ideal for someone with a background in machine learning who is comfortable navigating ambiguous problems and collaborating across teams. Exa's mission to enhance search capabilities positions this role at the forefront of applied AI innovation.
Joblaze insights
- Listed yesterday — first seen on Joblaze October 31, 2026. Last confirmed on Exa's careers page October 31, 2026.
- This exact title is also open at 1 other location at Exa: San Francisco, California.
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Machine Learning, data processing, vector databases, web crawling.
- What seniority level is this role?
- Exa targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Engineer, Index Intelligence role at Exa.
From the original posting
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.
We crawl the web continuously and cannot keep all of it, refresh all of it, or spend a GPU on all of it. Something has to decide which pages are worth indexing, which links are worth following, which pages say the same thing as pages we already have, what has gone stale, and what we should never have picked up. These decisions are worth more than most ranking work. The cheapest way to improve search is to stop indexing pages nobody should ever retrieve, and to start indexing the ones we are missing. Today they run on a mix of learned signals and thresholds someone picked.
We are looking for a research engineer to own the signals behind those decisions, and the decisions themselves.
Desired Experience
You are comfortable owning a problem with no ground truth, where the first job is defining what the right answer means
Hands-on ML experience with classifiers, rankers and calibration, plus strong data instincts at web scale
You think in end-to-end impact. A signal only counts if a decision changes and search gets better
You like working across teams. Crawling, indexing and retrieval all consume what you build
You care about the problem of finding high quality knowledge and recognize how important this is for the world
Example Projects
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
Standard company text repeated across Exa's postings is omitted here.