What makes this team unusual is its interdisciplinary core. You will work alongside measurement scientists (psychometrics, validity theory), ML researchers, and platform engineers—bringing together ML research, statistical rigor, and production engineering. We are looking for a Research Scientist who treats evaluation methodology itself as a first-class research problem—someone with deep technical fluency in preference learning, reward modeling, or calibration theory, and the drive to advance the field while solving real problems at scale. We're hiring at multiple levels (early-career to senior researchers). What unites all candidates is depth of thinking about evaluation as a research problem.

Arena Intelligence is seeking a variety of machine learning scientists to help advance how we evaluate and understand AI models. You’ll help design and analyse experiments that uncover what makes models useful, trustworthy and capable through human preference signals. Your work will contribute to the scientific foundations of understanding AI at scale. This role is deeply interdisciplinary. You’ll work closely with engineers, product teams, marketing and the broader research community to develop new methods for comparing models, analyzing preference data, and disentangling performance factors like style, reasoning, and robustness. Your work will inform both the public leaderboard and the tools we provide to model developers.