Senior Applied Scientist

Job Overview

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, Relativity One, 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 accountability are critical.

• Develop machine learning and generative AI models that ship as customer‑facing product features.
• Collaborate closely with engineers to write production‑quality code and contribute across the full model deployment lifecycle.
• Design and evaluate models that operate at very large scale, including search and retrieval systems spanning hundreds of millions to billions of documents.
• Contribute to internal standards, processes, and tooling for building, evaluating, and deploying generative AI systems.
• Partner with Product and Data teams to assemble, curate, and synthesize datasets for model development and evaluation.
• Conduct rigorous experimentation, model evaluation, and iteration to improve model quality, explainability, safety, and performance.
• Collaborate across AI, engineering, and product teams to ensure models integrate effectively into larger systems.
• Apply Relativity’s AI Principles to ensure responsible, fair, secure, and transparent AI development.
• Communicate complex data science and machine learning concepts clearly and effectively to collaborators with diverse technical backgrounds.

• Experience building search or retrieval systems operating at the scale of hundreds of millions of documents.
• Experience developing and applying generative AI models as part of larger, domain‑specific systems.
• Experience across the full machine learning lifecycle, including experimentation, evaluation, deployment, and iteration.
• Experience working in containerized environments using Kubernetes‑based tooling and workflows.
• Interest in or experience with the legal industry, eDiscovery, or the broader justice system.
• Strong programming ability in a language such as Python.
• Comfort working in UNIX‑based environments using command‑line tools.
• Ability to communicate complex data science concepts thoughtfully and inclusively to a wide range of stakeholders.
• Preferred:
  Master’s degree in Computer Science or a quantitative field plus 2 years of relevant industry experience OR Ph.D. in Computer Science or a quantitative field OR equivalent of 5 years of relevant academic and/or industry experience.
• Experience building and deploying systems that leverage large language models.
• Experience contributing to shared data science or ML engineering standards, tooling, or best practices.

• Impactful Mission:
  Your work directly contributes to improving outcomes across the global justice system by helping customers uncover critical insights in massive, complex datasets.
• AI at Real Scale:
  You’ll work on some of the largest and most complex AI systems in the legal technology market.
• Growth and
  
  Collaboration:
  
  You’ll collaborate closely with experienced applied scientists, engineers, and product leaders while continuing to grow your expertise in generative AI and production machine learning systems.
• Responsible AI Culture:
  You’ll be part of an organization deeply committed to building AI that is ethical, transparent, secure, and accountable.
• Inclusive Environment:
  We value diverse perspectives, backgrounds, and ways of thinking.

Competitive compensation, health and retirement benefits, discretionary time off (DTO), parental leave for primary and secondary caregivers, company‑wide breaks, wellness resources, and an equity program. 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 $146,000 to $218,000. 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.