Staff Applied Scientist Document Vision

Capable and Reliable

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.

What You'll Do

• Take on the hardest, most ambiguous problems in the portfolio and produce clarity: a well-specified approach, an evaluation that settles the question, a system that ships.
• Set standards that reach beyond your team: evaluation methods, modeling patterns, and quality bars adopted by scientists you've never worked with.
• Own readiness for flagship AI systems, from problem framing through efficacy studies and production monitoring, in partnership with engineering.
• Multiply the team through deep review and mentorship of senior and lead scientists.
• Advise Applied Science leadership on where the science is going and where we should invest.

What You Bring

• A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years of applied AI/ML experience.
• Years of production AI/ML behind you: systems you specified, shipped, and operated at scale, in partnership with the engineers who run them.
• Expert judgment about AI systems: you've built them, measured them, and formed views of their limits that hold up under challenge.
• Scientific rigor other people borrow: you supervise the data understanding of teams beyond your own, your evaluations become the template, and your error analyses end debates.
• Software-engineering judgment trusted across teams, and the programming skill to credibly prototype what you propose.
• An ownership mindset that extends across the organization.

Nice to Have

• Experience with vision-language or document-understanding models (layout analysis, OCR-adjacent pipelines, table and figure extraction) in production.
• An interest in legal technology and the justice system.
• Experience developing information retrieval systems.
• Experience developing agentic harnesses.
• Experience building reliable AI systems at scale.

Why Relativity Applied Science

This is the place where your curiosity, dedication, and talent will build products that power the pursuit of justice around the world.