We're looking for researchers to make frontier models and agents safer, with a strong focus on agent RL: training agents that stay safe and controllable in tool use, code execution, and long-horizon tasks. Design environments, rewards, and training data that reduce deception, reward hacking, and unauthorized actions, and teach agents to check authorization, recognize risks, and stop when needed, without sacrificing useful autonomy.