Job Description

Posting Type Remote / Hybrid Job Overview At Relativity, we're building a world-class Applied Science team to push the boundaries of intelligent systems in the legal domain. We're looking for a Staff Applied Scientist to join our team. Agentic AI-systems that perceive, think, and act-is not a far-off vision for us. It's already embedded in how Relativity aiR makes document review faster, more accurate, and more scalable than ever before. Our models interpret legal intent, reason across documents, cite their decisions, and automate thousands of hours of work-freeing legal professionals to focus on what matters most. This is your chance to contribute to the next generation of applied AI in legal tech : to build intelligent, safe, and defensible systems that scale human capability. Why This Work Matters Our Applied Science team are the brains behind Relativity aiR, the most scalable workflows in legal tech-automating decision-making in document review, privilege detection, and case strategy. These aren't just LLM wrappers; they're intelligent systems that reason, cite their thought process, and operate at scale across millions of documents. We're solving problems that matter in one of the most high-stakes domains out there. Our customers rely on us to build systems they can trust-systems that are auditable, defensible, and responsible by design. You'll help us extend those systems, validating AI decisions statistically, simplifying complexity for users, and pushing the boundaries of what's possible while keeping experts in the loop. Why Relativity? We're not just building products-we're building a better future for legal work. Our Applied Science team is 20 strong and growing. You'll be joining a thoughtful, kind, and technically excellent group who value impact, learning, and trust. This role is a chance to lead, contribute, and grow in a supportive and intellectually rich environment. Job Description and Requirements What You'll Do Write code that solves real customer problems and scales cleanly... Built to be easy to ship, operate , and maintain. Collaborate with fellow Applied Scientists... And with our Engineers, Product Managers, Designers, and Customers. Design and execute statistically sound experiments... Then automate them into reusable benchmarks. Rapidly build AI- and ML-powered prototypes... Then turn them into reliable, scalable production models. Select the right model for each task... Be it a decision tree or a frontier LLM. Stay grounded in evidence... A nd open to change. Who You Are 6-10+ years of professional experience in ML, Applied Science, or a closely related area. Hold a Master's or Ph.D. in a relevant field (e.g., Computer Science, Statistics, Applied Math) OR equivalent professional experience. Proven ability to move fast without breaking everything : you know how to prototype-and how to simplify for production. Comfortable reading and applying research; skeptical enough to validate the results. Experienced with a range of modeling techniques-from classic ML to large-scale generative models. Familiar with modern MLOps tooling (e.g., containers, workflow orchestration, telemetry, deployment patterns and experimentation ). Capable communicator, able to explain complex ideas to technical and non-technical stakeholders alike. Humble, curious , adaptable . Not afraid of failure. Not afraid to lead. Not afraid to ask questions. End-to-end owner - able to understand and learn about our problem space, devise solutions and bring them to market alongside our engineering, product and support organizations Strong Python Programmer, experienced in various data and machine learning libraries ( e.g. numpy , py torch , sc ikit-learn , pyspark ) 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 : 197,000 and $295,000 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. #J-18808-Ljbffr

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