Here at Relativity we prioritize flexibility and work-life harmony. Our Hybrid work environment provides options tailored to your role and location, aiming to enhance engagement, connectivity, and productivity.
Join us to experience a culture of collaboration and innovation, where connecting in-person adds value to our collective growth. Let's work together!
As an Applied Science intern, you will work on a project together with the support and mentorship of a host Applied Scientist. Whatever project you work on, you will be part of a diverse, interdisciplinary, collaborative team of designers, product managers, engineers, and other applied scientists.
We offer a 3-month, paid, full-time Summer Internship starting in July 2025. Your success will provide you with knowledge of applications of modern (Generative) AI and open many opportunities within our organization and beyond!
Job Description and Requirements
Your Role in Action
Our Applied Science interns work on a variety of purpose-driven projects. You might find yourself working on a graph analysis model identifying key actors and their communications, a reinforcement learning model powered by attorney feedback, or new search technology enabling investigators to find the smallest of needles in the most massive of haystacks. Your intern project will be motivated and measured by solving the needs of the members of the broad investigative and justice community.
Your Skills
Masters or Doctorate level student in a quantitative discipline
Fluency in Python
Analytical mindset
Knowledge of statistics and ML
Comfortable communicating and collaborating in an interdisciplinary environment
Interest in the legal industry and global justice systems
#LI-Hybrid
Relativity is a diverse workplace with different skills and life experiences—and we love and celebrate those differences. We believe that employees are happiest when they're empowered to be their full, authentic selves, regardless of how they identify.
Relativity is committed to competitive, fair, and equitable compensation practices.
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.