Research Scientist (Control)

Apollo Research is focused on addressing risks associated with AI capabilities, particularly in the context of loss of control and deceptive alignment. They are seeking a Research Scientist (Control) to join their AGI safety product team and help develop practical tools for AI control research, including the design and implementation of monitoring systems to enhance AI safety.

Responsibilities:

• Systematically collect and catalog coding agent failure modes from real-world instances, our internal deployments, public examples, research literature, and theoretical predictions
• Design and conduct experiments to test monitor effectiveness across different failure modes and agent behaviors
• Build and maintain evaluation frameworks to measure progress on monitoring capabilities
• Build and maintain high-quality datasets to train and test monitors on
• Iterate on monitoring approaches based on empirical results, balancing detection accuracy with computational efficiency
• Stay current with research on AI safety, agent failures, and detection methodologies
• Stay current with research into coding security and safety vulnerabilities
• Develop & maintain a comprehensive library of monitoring prompts tailored to specific failure modes (e.g., security vulnerabilities, goal misalignment, deceptive behaviors)
• Experiment with different reasoning strategies and output formats to improve monitor reliability
• Design and test hierarchical monitoring architectures and ensemble approaches
• Optimize log pre-processing pipelines to extract relevant signals while minimizing latency and computational costs
• Implement and evaluate different scaffolding approaches for monitors, including chain-of-thought reasoning, structured outputs, and multi-step verification
• Fine-tune open-source models to create efficient monitors for high-volume production environments
• Design and build agentic monitoring systems that autonomously investigate logs to identify both known and novel failure modes
• Build automated red-teaming pipelines that attack monitors at scale
• Design iterative adversarial games where a red-team and blue team continuously attack and defend respectively