Research Scientist, Agent Robustness
Scale AI•San Francisco, CA; New York, NY
Full-time
👁️ 7 views•📝 0 applications•Posted 4/27/2026•Expires 9/29/2026
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Job Description
Scale Labs, Research Scientist — Agent Robustness As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them; Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment; Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use; Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting tec
Required Skills
CommunicationCross-functional CollaborationLearning & DevelopmentMachine LearningPrototypingRecruitment
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