Senior Manager, Applied Science

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.

Your team will build for both, and you'll define the standard for how.

What You'll Do

• Lead and grow a team of applied scientists: hire, coach, set direction, and develop people toward their best work.
• Set the technical and scientific bar. The work stays hands-on: you'll shape architectures, review designs and evaluations, and dig into hard problems alongside your team, close enough to the science to lead by example.
• Own AI system readiness end-to-end, from problem framing through evaluation, error analysis, efficacy studies, and production monitoring, so that what ships is dependable and defensible.
• Choose the right problems. Current examples range from agentic assistants that extend what a legal professional can do, to large-scale review and analysis that must stay reliable across hundreds of thousands of documents per matter. You'll help decide where we invest.
• Partner with product, engineering, design, customer-facing teams, and the legal experts on the team to take ideas from proof-of-concept to production at scale.
• Communicate with precision to your team, to leadership, and to customers: translate technical nuance into decisions people can act on, and carry the customer's voice back into the work.
• Represent Relativity at industry conferences, events, and with customers.

What You Bring

• A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years in applied AI/ML, including at least 1 year as a people leader.
• Deep applied AI/ML and deployment engineering experience: you've built production-ready AI systems and owned them through their production lifecycle, partnering with engineering teams to keep them running reliably.
• Fluency with modern generative AI as a component of larger systems, and sound judgment about what it can and cannot do reliably.
• Machine-learning rigor, grounded in data understanding: careful evaluation, error analysis, and the statistical thinking to draw only the conclusions your data supports.
• Strong software-engineering judgment and programming skill.
• An ownership mindset that extends beyond your immediate team.

Nice to Have

• An interest in legal technology and the justice system.
• Experience hiring and growing a team.
• Experience developing information retrieval systems or agentic harnesses.
• An interest in 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.