Anthropic

[P] Data Engineer, Safeguards

Anthropic
US San Francisco, CA, US
Onsite 2026-07-02
Announced salary
$320,000 - $405,000
Low
$123K
Median
$161K
High
$211K
Market in San Francisco · BLS OEWS 2025
Estimated net pay
$16,846 - $20,625
/month · 37% withheld
after tax & contributions · Single, no dependents

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** ------------------ As a Data Engineer on the Safeguards team, you will build the data foundations that keep our AI systems safe. The Safeguards team works to monitor models, prevent misuse, and ensure user well\-being — and doing that well requires robust, reliable data infrastructure. In this role, you will design and build the pipelines, warehousing solutions, and analytical tooling that power our safety and trust efforts at scale. You'll work closely with engineers, data scientists, and policy teams to ensure the Safeguards organization has the data it needs to detect abuse patterns, measure the effectiveness of safety interventions, and make informed decisions about model behavior and enforcement. This is a high\-impact role where your work directly supports Anthropic's mission to develop AI that is safe and beneficial. **Key responsibilities** ------------------------ * Design, build, and maintain scalable data pipelines that support safety monitoring, abuse detection, and enforcement workflows * Develop and optimize data models and warehousing solutions to enable efficient analysis of large\-scale usage and safety data * Build and maintain dashboards and reporting infrastructure that give Safeguards teams visibility into model behavior, misuse patterns, and enforcement outcomes * Collaborate with engineers to integrate data from multiple sources — including model outputs, user reports, and automated classifiers — into a unified analytical layer * Implement data quality frameworks, monitoring, and alerting to ensure the reliability of safety\-critical data * Partner with re

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