Negotiable
Outside
Remote
Remote
Summary: The AI Offense-Defense Dynamics Lead Researcher will spearhead research aimed at understanding the complex dynamics of AI systems and their societal impacts. This role involves developing frameworks to analyze how AI technologies can either enhance safety or pose risks, providing insights for various stakeholders. The position is fully remote, with occasional travel required, and offers a unique opportunity to influence AI governance and risk management. The researcher will utilize interdisciplinary methods to produce actionable guidance for policymakers and developers.
Key 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
Key Skills:
- 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
Salary (Rate): undetermined
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: Other
Job Summary
About CARMA
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
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
Pluses
- PhD in a relevant field
- Experience with system dynamics modeling, hypergraph techniques, or other complex network analysis methods
- Skills in developing interactive tools or dashboards for risk visualization and communication
- Background in interdisciplinary research bridging technical and social science domains
- Demonstrated aptitude in top-down techniques and first-principles thinking
- Experience with the quantification of qualitative risk factors or developing proxy metrics for complex phenomena
- Background in compiling and analyzing incident databases or case studies for pattern recognition
- Familiarity with empirical approaches to technology assessment and impact prediction
- Knowledge of international relations theory as it applies to technology proliferation dynamics