Applied Science Engineering Jobs (NOW HIRING)

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 Senior Manager, Applied Science to lead a team expanding 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.

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.

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:

$208,000 and $312,000

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:

Algorithms, Data Science, Natural Language, Predictive Analytics, Project Management, Reinforcement Learning, Research Development, Science, Statistical Models, Team Leadership