Founding Staff, DC Office, (Science Communications, Engagement and Policy Research)
Position Summary & Responsibilities
About Apollo Research
Apollo Research is a mission-driven startup committed to reducing the threats from scheming frontier AI systems. We work with and are trusted by every frontier AI lab. We do fundamental research, test their models before deployment, and collaborate with them on scheming mitigations. The Governance team conducts research and provides advice on the institutional, legal and strategic implications of AI scheming and loss of control threats, working closely alongside our Science of Scheming, Evaluations, and Monitoring teams.
The Governance team works on a bet: that the national security interventions the U.S. government enacts and implements over the next 15 months, through legislation, procurement and verifiable procedures for testing, monitoring, controlling and pacing frontier AI, will be among the most significant contributions to our collective ability to stay in control of increasingly advanced AI systems.
That means our DC work focuses on identifying, describing and offering policy solutions to some of the hardest open problems at the intersection of AI misalignment and national security: loss of control, evaluation-aware models that stand to undermine existing assessment regimes, and the handoff of the development of AI systems to AIs themselves. We are routinely requested to brief decision makers in the U.S. governmentβs executive and legislative branch and across multiple intelligence agencies, as well as to provide input into relevant federal and state legislation and procurement efforts. The majority of our work is non-public, including privately held demonstrations and briefing materials written for decision makers across key nodes.
Our public facing output includes Assurance of Frontier AI Built for National Security, a roadmap for DoW, ODNI, NIST and CAISI to operationalize AI model re
Key Role Competencies & Evaluation Highlights
Rigorous assessment of frontier AI safety mechanisms, mechanistic interpretability of neural networks, and academic research rigor.
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