Safeguards Enforcement Lead, Frontier Abuse Enforcement
AnthropicSan Francisco, CA | New York City, NY | Washington, DCPosted 24 March 2026
Tech Stack
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
Anthropic is an AI safety and research company working to build reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our customers and society as a whole. As a Safeguards Enforcement Lead focusing on frontier model abuse, you will serve as the dedicated program owner for one of the most consequential and fast-moving abuse vectors on our platform: actors who misuse Anthropic's models to train competing AI systems in violation of our usage policies and terms of service. Safety is core to our mission, and you'll own how we identify, investigate, and act against this category of harm — from developing the detection playbook to seeing enforcement cases through to resolution.
Important Context:
In this position, you may be exposed to and engage with explicit content spanning a range of topics, including those of a sexual, violent, or psychologically disturbing nature. There is also an on-call responsibility across the Policy and Enforcement teams.
Responsibilities:
Own the end-to-end enforcement strategy against unauthorized frontier model abuse — from detection signal development through enforcement action and post-enforcement measurement
Operationalize detection and review pipelines — translating leads from case investigations and detection outputs into structured review workflows that scale across surfaces
Drive enforcement actions for high-priority actors — synthesizing signals from across intelligence, detection, and review into enforcement packages suitable for formal escalation
Partner with Legal and Policy to assess case strength, characterize ToS and IP violations, and support enforcement escalations through formal channels
Close the loop between enforcement outcomes and upstream improvements — channeling review results and enforcement findings back to policy updates and detection refinements
Develop and maintain a dynamic enforcement framework that accounts for the complexity of cross-surface enforcement — including varied escalation paths, partner coordination, and enforcement consistency across surfaces
Collaborate with Threat Intelligence, Research, Engineering, and Policy partners to ensure detection coverage keeps pace with evolving frontier abuse tactics
Maintain rigorous documentation of enforcement decisions, pipeline logic, and precedents to build institutional knowledge
Qualifications:
Required
5+ years of experience in trust safety, abuse enforcement, fraud investigation, policy, or a related field — with demonstrated ownership of complex, high-stakes enforcement programs
Track record of building detection and enforcement approaches for novel or emerging abuse vectors where established playbooks don't exist
Experience supporting or directly contributing to formal enforcement actions — including case documentation, evidence packaging, and escalation coordination
Strong data analysis skills — comfortable navigating complex, multi-table datasets to surface behavioral patterns and support investigations
Experience conducting structured investigations, including open-source intelligence techniques and cross-referencing external data sources to attribute activity
Demonstrated ability to translate ambiguous policy questions into defensible enforcement decisions and clear written findings
Strong written and verbal communication skills — able to present complex enforcement cases clearly to stakeholders across Legal, Policy, and Engineering
Preferred
Familiarity with the AI/ML ecosystem — including how model distillation, fine-tuning, and synthetic data generation work in practice, and how actors attempt to obscu ... (truncated, view full listing at source)
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