Principal Applied Scientist

Relativity

5d•Remote

About The Position

Relativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high‑stakes legal work where accuracy, trust, and defensibility are essential. Relativity aiR is redefining document review through agentic AI systems that reason, cite their decisions, and scale across millions of documents. These systems automate complex legal workflows while keeping humans in the loop, enabling legal professionals to focus on what matters most. At Relativity, we are building a world‑class Applied Science organization focused on pushing the boundaries of intelligent systems in one of the most demanding and consequential domains: the legal system. The Applied Science team sits at the core of Relativity’s AI development. We are responsible for designing, validating, and operating the intelligent systems behind Relativity aiR. Our work goes far beyond simple model integrations. We build agentic systems that reason over documents, validate decisions statistically, remain auditable and defensible, and operate reliably at massive scale. Trust, reliability, and responsibility are foundational to everything we build. Our team values curiosity, experimentation, rigor, and collaboration. We move quickly, validate assumptions with evidence, and simplify aggressively to deliver systems that are safe, reliable, and impactful in production.

Requirements

8+ years of professional experience in applied science, machine learning, or a closely related field.
Master’s or Ph.D. in Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline, or equivalent professional experience.
Proven ability to move quickly from prototype to production, simplifying complex ideas into robust systems.
Experience reading, validating, and applying research with a healthy level of skepticism.
Experience across a wide range of modeling techniques, from classical machine learning to large‑scale generative models.
Familiarity with modern MLOps tooling and practices, including containers, workflow orchestration, deployment patterns, telemetry, and experimentation systems.
Strong Python programming skills and experience with common data and ML libraries such as numpy, PyTorch, scikit‑learn, and PySpark.
Strong communication skills, with the ability to explain complex technical concepts clearly to both technical and non‑technical audiences.
End‑to‑end ownership mindset, with the ability to understand new problem spaces, design solutions, and bring them to market alongside engineering, product, and support partners.
A collaborative, curious, and adaptable approach, with comfort leading, questioning assumptions, and learning from failure.

Nice To Haves

Algorithms
Data Analysis
Machine Learning (ML)
Natural Language
Python (Programming Language)
Reinforcement Learning
Researching
Scientific Writing
Statistical Models
Technical Leadership

Responsibilities

Lead the design and validation of intelligent systems that customers can trust in high‑stakes legal workflows.
Operate end‑to‑end: understanding the problem space, designing solutions, validating them statistically, and bringing them to production in partnership with engineering, product, and customer‑facing teams.
Write production‑quality code that solves real customer problems and scales cleanly, with systems designed to be easy to ship, operate, and maintain.
Collaborate closely with fellow Applied Scientists as well as Engineers, Product Managers, Designers, and Customers.
Design and execute statistically sound experiments and automate them into reusable benchmarks and evaluation frameworks.
Rapidly prototype AI‑ and ML‑powered solutions and mature them into reliable, scalable production models.
Select the appropriate modeling approach for each problem, ranging from classical machine learning techniques to frontier large language models.
Validate model behavior rigorously using evidence, metrics, and experimentation, remaining open to changing course when the data demands it.
Contribute to building intelligent systems that reason, cite their decisions, and operate defensibly at scale.
Help push the boundaries of agentic AI while ensuring systems remain auditable, reliable, and responsible.