Safeguards Enforcement Analyst, User Well-being

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 As a Safeguards Analyst on the User Well-being team, you will be focused on supporting the design and deployment of mental health guardrails – iterating on detection systems, managing review queues, evaluating new interventions, and monitoring existing ones. Interventions range from steering how Claude responds in the conversation itself to in-product features that connect users to other resources. This work involves translating expert clinical guidance, internal data analyses, and legal or engineering constraints into concrete changes to how we detect, review, and respond. This team covers a broad set of interconnected harms including suicide, self-harm, disordered eating, AI sycophancy, and emotional dependence on AI. This position may expand into broader areas of user well-being enforcement over time. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way. *Important context for this role: 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. Key responsibilities - Support the design and execution of interventions, defining key metrics, and curating evaluation datasets - Partner with Engineering and Data Science teams to build, tune, and validate detection models for automated intervention systems, including threshold-setting and precision and recall tradeoffs - Monitor how interventions and detection systems perform over time - Review flagged content to drive enforcement and policy improvements - Support the development of in-product features that connect users to crisis resources, working with Product, Legal, and external partners on referral pathways and user-facing content - Support the Safeguards Policy Design team by providing detailed feedback on policy gaps based on real scenarios - Keep up to date with emerging AI policy and external research on AI's relationship to mental health, and use these to inform our decision-making and workflows Minimum qualifications - Experience in trust & safety, product policy, content moderation, or a related field, with direct exposure to mental health, suicide and self-harm, or related well-being harm areas - Experience designing or running experiments, evaluations, or measurement studies to determine whether an intervention worked - Experience translating policy definitions into measurable form — the rubrics, review guidelines, or classification criteria, whether applied by human reviewers or automated systems - Experience managing or coordinating content review operations, including quality assurance and workflow management - Proficiency in SQL and/or other data analysis tools to measure intervention efficacy, monitor workflow health, and surface policy gaps - Experience working with generative AI products, including writing effective prompts for content review, classification, or evaluation - Experience turning open questions and data into concise and insightful analysis - Experience identifying emerging risks and communicating findings to cross-functional stakeholders - Understanding of the challenges involved in implementing product policies at scale in the content moderation space - Sound judgment in ambiguous, high-consequence cases, and comfort making a call and escalating appropriately when the available signal is incomplete Preferred qualifications - Subject matter expertise in mental health, whether developed in academia, clinical practice, crisis intervention, trust & safety, or other related settings - Experience building or evaluating LLM-based classification systems - Experience using agentic tools (e.g. Claude Code) to scale analysis or automate recurring work - Experience working within crisis support The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $245,000—$285,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Minimum qualifications include experience in trust & safety, product policy, content moderation, or related field with exposure to mental health, suicide, self-harm, or related well-being harms; experience designing or running experiments or evaluation studies to assess intervention effectiveness; experience translating policy definitions into measurable rubrics or guidelines; experience coordinating content review operations including quality assurance and workflow management; proficiency in SQL and/or other data analysis tools to measure intervention efficacy and monitor workflow health; experience working with generative AI products including writing effective prompts for content review, classification, or evaluation; ability to turn open questions and data into concise analysis; experience identifying emerging risks and communicating findings to cross-functional stakeholders; understanding of scaling product policies in content moderation; sound judgment in ambiguous, high-consequence cases with appropriate escalation when signals are incomplete. Preferred qualifications include subject matter expertise in mental health and experience with LLM-based classification systems and crisis support tools.

Support the design and execution of interventions, defining key metrics, and curating evaluation datasets; Partner with Engineering and Data Science teams to build, tune, and validate detection models for automated intervention systems, including threshold-setting and precision/recall tradeoffs; Monitor how interventions and detection systems perform over time; Review flagged content to drive enforcement and policy improvements; Support the development of in-product features that connect users to crisis resources, coordinating with Product, Legal, and external partners on referral pathways and user-facing content; Provide detailed feedback on policy gaps based on real scenarios to the Safeguards Policy Design team; Keep up to date with emerging AI policy and external research on AI and mental health to inform decision-making and workflows.