Search & Relevance Engineer →

Our search runs over a corpus that changes every minute. Your job is to make it fast, make it relevant, and make the improvement measurable. That spans index design, query understanding, ranking, and the evaluation work that separates a real gain from a convincing demo.
About NewsMesh

NewsMesh turns the world's news into clean, structured, real-time data. We ingest thousands of sources continuously, enrich every article with category, topics, people, and country relevance, and serve it through an API used by developers and, increasingly, by AI agents that discover and pay for it on their own. We are a small, senior team, and the work is measured by one thing: whether the data is right, current, and easy to use.
What you'll do

Design and tune search relevance over a large, constantly changing corpus
Own evaluation: define precision and recall, measure honestly, improve deliberately
Improve entity and topic extraction so filters stay trustworthy
What we look for

Experience with production search (Typesense, Elasticsearch, a vector DB, or similar)
Solid information-retrieval fundamentals
Rigorous about evaluation, not just shipping a model
Comfortable in Python and working with large datasets
Experience with embeddings, ranking, or learning-to-rank
Worked on dedup, clustering, or entity resolution
Compensation

Depending on experience, the expected pay range is $170K–$230K.
What we offer

Competitive pay, in the range listed above
Remote across the US
Real ownership: you will ship work customers depend on
A small, senior team with high standards and little process
How we hire

We read every application. If there is a fit, expect a short intro call, a practical exercise rooted in the kind of work you would actually do, and a few conversations with the people you would work with. A link to something you have built beats a resume.

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