AI Offense-Defense Dynamics Lead Researcher
New
AnywhereFull-TimeSenior
Salary125,000 - 200,000 USD per year
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Job Details
- Required Skills
- CybersecurityLLM
Requirements
- A M.Sc. or higher in either Computer Science, Cybersecurity, Criminology, Security Studies, AI Policy, Risk Management, or a related field
- Demonstrated experience with complex systems modeling, risk assessment methodologies, or security analysis
- Strong understanding of dual-use technologies and the factors that influence whether capabilities favor offensive or defensive applications
- Deep understanding of modern AI systems, including large language models, multimodal models, and autonomous agents, with ability to analyze their technical architectures and capability profiles
- Experience in any of the following: Security mindset, Security studies research, Cybersecurity, Safety engineering, AI governance, Operational risk management, Systems dynamics modeling, Network theory, Complexity science, Adversarial analysis, or Technical standards development
- Ability to develop both qualitative frameworks and quantitative models that capture sociotechnical interactions, and comfort creating semi-quantitative semi-empirical models also grounded in logic
- Record of relevant publications or research contributions related to technology risk, governance, or security
- Exceptional analytical thinking with ability to identify non-obvious path dependencies and feedback loops in complex systems
Responsibilities
- Develop quantitative system dynamics models capturing the interrelationships between technological, social, and institutional factors that influence AI risk landscapes
- Design detailed analytical models and simulations to identify critical leverage points where policy interventions could shift offense-defense balances toward safer outcomes
- Expand and operationalize our current offense/defense dynamics taxonomy and nascent framework, developing metrics and models to predict whether specific AI system features favor offensive or defensive applications
- Build empirically-informed analytical frameworks using documented cases of AI misuse and beneficial deployed uses to validate theoretical models
- Research how specific technical characteristics (capabilities breadth/depth, accessibility, adaptability, etc.) interact with sociotechnical contexts to determine offense-defense balances
- Build public understanding of offense-defense dynamics through blog posts, articles, conference talks, and media engagement
- Create tools and methodologies to assess new AI models upon release for their likely offense-defense implications
- Draft evidence-based guidance for AI governance that accounts for complex interdependencies between technological capabilities and deployment contexts
- Translate research findings into actionable guidance for key stakeholders including policymakers, AI developers, security professionals, and standards organizations
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