Remote Human Performance Exercise Science Jobs in Wisconsin

Remote/Hybrid

At Relativity, we build technology that helps people uncover the truth in complex data. Our software (SaaS) empowers legal professionals, governments, and organizations around the world to navigate high stakes matters with confidence, clarity, and integrity. By combining advanced AI, powerful analytics, and cloud-based technology, we help teams make sense of massive volumes of information and move critical work forward faster and more accurately. Every role at Relativity contributes to creating scalable, secure, and intelligent solutions with real-world impact-while fostering a culture where curiosity, collaboration, and inclusion thrive and where employees help shape the future of legal technology.

The AI and Applied Sciences department at Relativity drives innovation by developing advanced AI solutions and applied research to solve complex legal and compliance challenges.

The Legal Data Intelligence SME will bring expertise as a practicing litigator and deep knowledge of discovery. You'll draw on this expertise to help us build and evaluate our key generative AI capabilities. You'll interface with other litigators, both helping us explain how our technology operates to them and taking their feedback and providing it back to our applied science team, conveying the nuances of both the technology and the needs of the litigator persona as you do so. This role bridges legal expertise with technical innovation, guiding the design and deployment of solutions that optimize data intelligence for legal workflows.

• Contribute to Relativity applied science strategy by leveraging your subject matter expertise to design, build and improve our tests, benchmarks, and other evaluation systems for our generative AI solutions.
• Work internally with our applied scientists, product managers, designers, engineers, and customer enablement teams to provide internal feedback and suggestions on the enablement, performance, usability, and functionality of our generative AI solutions.
• Work externally with our clients and our industry to explain how our generative AI systems work and to receive their feedback on the performance of our generative AI in analyzing and classifying documents, identifying information that needs to be protected, and extracting and curating knowledge.
• Develop and deliver training and documentation for internal teams on legal data intelligence workflows and best practices.

• A Juris Doctorate and at least 3-5 years of experience as a practicing attorney; some but not all of this requirement may be satisfied by equivalent experience working for a legal service provider or legal technology vendor.
• Deep interest in the use of generative AI to responsibly advance the practice of law and access to justice.
• Strong and clear communicator, able to synthesize and communicate complex technical concepts and results to internal and external audiences.
• Experience in discovery or legal technology platforms.
• Experience working with cross-functional teams.

• Significant litigation experience and deep understanding of the processes of investigations and litigation, including discovery and pre-trial practice.
• At least 5 years of experience as a practicing attorney.
• Strong understanding of the traditional EDRM and broader Legal Data Intelligence use cases and workflows.

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 $134,000 and $202,000.

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.

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