Technical Policy Manager, Cyber Harms
AnthropicRemote-Friendly (Travel-Required) | San Francisco, CA | Washington, DCPosted 24 February 2026
Job Description
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the Role:
We are looking for a cybersecurity expert to lead our efforts to prevent AI misuse in the cyber domain. As a Cyber Harms Technical Policy Manager, you will lead a team applying deep technical expertise to inform the design of safety systems that detect harmful cyber behaviors and prevent misuse by sophisticated threat actors. Working closely with Research Engineers who build these safety systems, you and your team will provide the critical cybersecurity domain knowledge needed to ensure our safeguards are effective against real-world threats. You will be at the forefront of defining what responsible AI safety looks like in the cybersecurity domain, working across research, policy, and engineering to translate complex cyber threat concepts into concrete technical safeguards and actionable policies. This is a unique opportunity to shape how frontier AI models handle dual-use cybersecurity knowledge—balancing the tremendous potential of AI to advance legitimate security research and defensive capabilities while preventing misuse by malicious actors.
In this role, you will:
Lead and grow a team of technical specialists focused on cyber threat modeling and evaluation frameworks
Design and oversee execution of capability evaluations ("evals") to assess the cyber-relevant capabilities of new models
Create comprehensive cyber threat models, including attack vectors, exploit chains, precursor identification, and weaponization techniques
Develop and iterate on usage policies that govern responsible use of our models for emerging capabilities and use cases related to cyber harms
Serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies
Collaborate closely with internal and external threat modeling experts to develop training data for safety systems, and with ML engineers to train these systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers
Analyze safety system performance in traffic, identifying gaps and proposing improvements
Conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks
Develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces
Partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle
Translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies
Contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety
Monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these
Mentor and develop team members, fostering a culture of technical excellence and responsible AI development
You may be a good fit if you have:
An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity
5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing
2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders
Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
Deep expe ... (truncated, view full listing at source)
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