AI Offense-Defense Dynamics Lead Researcher

at Future of Life Institute
  • $125k-$200k
  • Remote

Remote

All Others

Senior

Job description

Job Summary

Lead cutting-edge research on offense-defense dynamics of advanced AI systems, examining how specific features of AI technologies influence their propensity to either enhance societal safety or amplify risks. Apply interdisciplinary methods to develop quantitative and qualitative frameworks for analyzing how AI capabilities proliferate through society as either protective or harmful applications. Your work will produce actionable insights for AI developers, evaluators, standards bodies, and policymakers to anticipate and mitigate risks before high-consequence capabilities proliferate.

This position offers a unique opportunity to develop pioneering frameworks that will shape how society evaluates and governs increasingly powerful AI systems, with direct impact on global efforts to maximize AI’s benefits while minimizing its risks. This role is 100% remote but requires occasional travel.

About CARMA

The Center for AI Risk Management & Alignment (CARMA) works to help society navigate the complex and potentially catastrophic risks arising from increasingly powerful AI systems. Our mission is specifically to lower the risks to humanity and the biosphere from transformative AI.

We focus on grounding AI risk management in rigorous analysis, developing policy frameworks that squarely address AGI, advancing technical safety approaches, and fostering global perspectives on durable safety. Through these complementary approaches, CARMA aims to provide critical support to society for managing the outsized risks from advanced AI before they materialize.

CARMA is a fiscally-sponsored project of Social & Environmental Entrepreneurs, Inc., a 501©(3) nonprofit public benefit corporation.

Responsibilities

• Develop quantitative system dynamics models capturing the interrelationships between technological, social, and institutional factors that influence AI risk landscapes

• 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

• Design detailed analytical models and simulations to identify critical leverage points where policy interventions could shift offense-defense balances toward safer outcomes

• Build empirically-informed analytical frameworks based on real-world AI incidents and use cases to validate theoretical models

• Research how specific technical characteristics (capabilities breadth/depth, accessibility, adaptability, etc.) interact with sociotechnical contexts to determine offense-defense balances

• Effectively communicate through public speaking, blog posts, articles, and other media to build fields’ understanding and traction regarding the societal framing of offense-defense dynamics

• 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

• Communicate findings effectively to diverse 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

• 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

CARMA/SEE is proud to be an Equal Opportunity Employer. We will not discriminate on the basis of race, ethnicity, sex, age, religion, gender reassignment, partnership status, maternity, or sexual orientation. We are, by policy and action, an inclusive organization and actively promote equal opportunities for all humans with the right mix of talent, knowledge, skills, attitude, and potential, so hiring is only based on individual merit for the job. Our organization operates through a fiscal sponsor whose infrastructure only supports persons authorized to work in the U.S. as employees. Candidates outside the U.S. would be engaged as independent contractors with project-focused responsibilities. Note that we are unable to sponsor visas at this time.

$125,000 - $200,000 a year

plus good benefits if U.S. employee

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