Threat Modeler Lead, Cbrn, Deepmind

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

Lead Threat Modeler for CBRN risks in advanced AI models at DeepMind, focusing on evaluating and mitigating dual-use risks to inform model releases and ensure safety-critical deployments. This role requires operational discipline to analyze complex scientific data and apply real-world threat intelligence to AI safety.

What you'd actually do

  1. Refine and maintain the threat modeling 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. Engage 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, or 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.
  • 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

  • critical thresholds
  • high-capability AI models
  • deployment safeguards
  • external stakeholders
  • dual-use risks
  • AI safety
  • evaluating
  • LLMs
  • vulnerabilities
  • misuse scenarios
  • CBRNe domains
  • critical risks and vulnerabilities

Other signals

  • AI safety
  • risk assessment
  • dual-use risks
  • threat modeling
  • evaluations