Lead Applied Scientist, Public Sector Investigations

Posting Type

Remote/Hybrid

Job Overview

The Work

Every legal matter is its own experiment. An attorney arrives with a theory of the case; the evidence arrives as hundreds of thousands of documents, sometimes millions, that no one has read and no model has seen. Somewhere in the cross product of the two are the answers that decide lawsuits, investigations, and livelihoods. Finding them quickly and defensibly, with the integrity and credibility attorneys can rely on, is the problem we own. We solve it creatively and rigorously.

Relativity is a data-centered, AI-native legal technology company, and Applied Science builds the AI inside Relativity aiR. We launched aiR in 2023 and have now run commercial generative AI in the legal domain for more than three years, powering work that includes the largest investigations in the world. Our systems are distinguished by the data they operate over (more than 93 petabytes) and the work they have done: over 190 million AI review decisions, backed by more than 1 billion generative sub-analyses in 2026 alone. The team is as distinctive as the data: legal experts, all former litigators, work directly inside Applied Science.

At Relativity, our mission is to Organize data. Discover the truth. Act on it. The Applied Science team serves this mission by building bold and ambitious AI systems. We are curious, dedicated, and humble. We understand complexity, uphold rigor, and measure relentlessly. We build and ship with pace. Above all, we are interdisciplinary collaborators and team players.

We're looking for a Lead Applied Scientist to set technical direction for our public-sector investigations work as we expand the aiR agentic harness for greater capability and reliability.

Job Description and Requirements

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 set the direction for how your workstream does it.

The Focus: Public Sector Investigations

This role anchors our science for public-sector investigations: FOIA and public-records workflows, government investigations, and the agencies that run them. Records officers and investigators face the same combinatorial problem as litigators, with an added obligation: their results answer to the public. You'll own the science for this domain end-to-end, from retrieval and review systems to the evaluation standards that make responses complete, correct, and defensible.

What You'll Do

• Set technical direction for the workstream: choose the approaches, sequence the science, and keep the work coherent across contributors.
• Lead the science without managing the people: review other scientists' designs and evaluations, raise the bar, and unblock the hard problems.
• Own AI system readiness across the workstream, from problem framing through evaluation, error analysis, efficacy studies, and production monitoring, in partnership with engineering.
• Be the first escalation for the domain's hardest technical calls, and know when to bring in Staff and Principal partners.
• Be a pathfinder: build the first prototypes with tiger teams, and carry early initiatives from idea to evidence.
• Take the work from proof-of-concept to production at scale, working with product, engineering, design, and the legal experts embedded in the team.

What You Bring

• A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 3 years of professional experience in applied AI/ML.
• Eligibility for a US Public Trust clearance.
• Working knowledge of privacy and records standards: FOIA exemptions, PII handling, and redaction.
• Deep applied AI/ML and deployment experience: AI systems you specified, prototyped, and carried into production with engineering partners.
• Enough depth in modern AI to direct others' work with it: you can tell a sound AI system from a fragile one before it ships.
• You lead the data understanding for your workstream: you design the study that settles the argument, and you trust data over intuition.
• Software-engineering judgment that sets a workstream's tone: your reviews teach, and your designs last.
• An ownership mindset that extends beyond your immediate scope.

Nice to Have

• Deeper experience with government data or public-records work.
• 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.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$164,000 and $246,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position. 

Required Skills:

Communication, Database Management, Data Governance, Government Regulation, Information Security, Legal Practices, Legal Research, Policy Analysis, Stakeholder Management, Strategic Planning