About the Role
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 focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments.
Responsibilities
- Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs
- Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected
- Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps
- Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation
Requirements
- 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 technical research collaboratively. You should be comfortable designing control and monitoring experiments for AI systems, building prototype systems, and quickly turning new ideas from the research literature into working prototypes.
- A track record of published research in machine learning, particularly in generative AI.
- At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
- Strong written and verbal communication skills to operate in a cross-functional team.
Qualifications
- Experience with runtime monitoring, anomaly detection, or observability for ML systems.
- Familiarity with AI control or alignment research (e.g., scalable oversight, interpretability, debate).
- Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.