Threat Modeler Lead, Cbrne, Deepmind

Google Google · Big Tech · New York, NY +2

Lead threat modeler for AI safety in CBRNE domains, focusing on evaluating and mitigating dual-use risks of advanced AI models. This role involves refining threat modeling frameworks, designing evaluations for AI risks, collaborating with mitigation teams, and engaging with external stakeholders. Requires a PhD and experience in national labs or defense organizations, with a preference for experience in red-teaming LLMs and understanding CBRNE risks.

What you'd actually do

  1. Refine and maintain the threat modelling framework to define critical thresholds, and integrate these models into FSF review processes to support deployment decision-making.
  2. Partner cross-functionally to design and implement evaluations identifying CBRNe-related risks in high-capability AI models.
  3. Collaborate across evaluation and mitigation teams to determine if deployment safeguards are adequate for high-capability models.
  4. Engagement with external stakeholders including governmental entities, third party organizations (including the Frontier Model Forum) and external subject matter experts.
  5. Monitor the external engaged landscape, emerging dual-use methodologies and broader frontier AI domain.

Skills

Required

  • PhD degree in Science, Engineering, Data Science, a related field, or equivalent practical experience.
  • 2 years of experience within a national laboratory, government defense organization, military intelligence unit, or specialized research institution.
  • Experience communicating deeply complex, high-consequence technical CBRNe risks into clear, actionable insights for business leaders, corporate governance bodies, and policy experts.
  • Experience executing independent projects and synthesizing large datasets under tight deadlines in fast-paced environments.
  • Experience understanding complex issues through qualitative/quantitative models for decision-making with imprecise data.

Nice to have

  • Experience red-teaming, evaluating, utilizing LLMs to identify systematic vulnerabilities and potential misuse scenarios.
  • Knowledge of dual-risk components of CBRNe domains, with emphasis on biological risks and laboratory/defense paradigms.
  • Track record of reviewing complex technical reports and identifying critical risks and vulnerabilities under tight timeframes.

What the JD emphasized

  • rigorous risk-calibration
  • operational discipline
  • real-world threat intelligence
  • safety-critical deployments
  • evaluating AI risks
  • dual-use risks
  • red-teaming
  • evaluating
  • LLMs to identify systematic vulnerabilities and potential misuse scenarios
  • CBRNe domains
  • biological risks
  • laboratory/defense paradigms
  • critical risks and vulnerabilities

Other signals

  • AI risk evaluation
  • dual-use risks
  • safety-critical deployments
  • threat modeling framework
  • CBRNe domains