Two requests can look nearly identical and be worlds apart. “See if you can find me an example of this” needs a capable system: it finds the example or it doesn’t. “Conduct a reasonable search for any and all documents responsive to this request” is a different kind of promise. Its answer spans a corpus no one will ever read end-to-end. So the system’s process, as much as its output, has to earn the trust of the professionals who rely on it.
That property is reliability. It decomposes into consistency, robustness, calibration, and safety: systems that behave tomorrow the way they did today, degrade predictably under stress, know how confident they should be, and check their own work. Before aiR returns an analysis, it validates its citations and runs internal consistency checks; when a check fails, it refuses to answer. It has refused more than a million times so far in 2026, and we count every one as a success: an error caught before it reached a user.
You’ll build for both, and help define the standard for how.
The Focus: Document Vision
This role anchors our document-vision work: teaching systems to read evidence the way legal professionals do. Real matters arrive as scanned pages, photographs, tables, handwriting, stamps, and broken layouts, at the scale of millions of documents. You’ll own the science of multimodal document understanding across aiR, from vision-language modeling to the evaluation standards that make visual evidence usable and defensible.