Anthropic · Engineering · Staff+ · Posted 2026-07-13
Staff+ Software Engineer, Safeguards Human Review Tooling
Anthropic · New York City, NY; San Francisco, CA · $320k–485k base
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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
The Safeguards team is responsible for ensuring our models and products are developed and deployed safely. We're looking for engineers for our Review Tooling team, which builds the systems that humans — and increasingly Claude — use to investigate potential harms and take enforcement actions across Anthropic's first-party products and third-party cloud platforms.
You'll own the tools our safety investigators rely on to understand what's happening on our platforms and act on it, as well as the platform underneath those tools. That platform includes data analysis capabilities, privacy-preserving primitives that keep review workflows compatible with our data retention commitments, and a sandbox environment where new review interfaces and workflows can be built and iterated quickly.
These are internal tools, but they are anything but low-stakes: the speed, clarity, and reliability of this tooling directly determines how quickly Anthropic can identify harmful behavior, make sound enforcement decisions, and feed signals back into model training and safety classifiers. You'll partner closely with policy, operations, data science, legal, and privacy teams to ensure our enforcement systems are effective, accurate, and trustworthy.
Key responsibilities
Build investigation, review, and enforcement tooling for both first-party and third-party platform surfaces — including case queues, investigation views, decision and audit logging, and account-actioning workflows
Develop the platform layer of reusable APIs, data storage, and backend services that lets new review workflows be stood up quickly and safely
Stand up and run deployments of this tooling across multiple clouds, including inside cloud-provider partner environments where data must stay in place. Keep the deployments consistent through shared deployment pipelines, smoke tests, observability, and alerting
Scale review through automation, including enabling reviewers to use Claude effectively and building toward Claude-assisted and Claude-driven review workflows
Partner with policy, operations, legal, privacy, and data science stakeholders to translate enforcement and investigation needs into reliable, well-designed systems that measurably reduce handling time and decision error
Instrument the tools you ship — surfacing metrics on queue health, reviewer throughput, and decision quality — and ensure tooling evolves alongside new privacy primitives and data retention commitments
Minimum qualifications
A technical background in full-stack or platform engineering, with the ability to engage deeply in architecture and design discussions
Experience shipping internal tools or platforms with demanding operational users, and a track record of improving their workflows measurably
Experience working cross-functionally with non-engineering partners such as operations, policy, or legal teams
Excellent communication skills, including the ability to explain technical tradeoffs to non-technical stakeholders
Care about the societal impacts of AI and want your work to make powerful systems safer
Preferred qualifications
8+ years of industry software engineering experience
Experience building data labeling, trust and safety, integrity, fraud, or abuse-prevention tooling, or other systems supporting human review at scale
Have built systems that handle sensitive or regulated data, with requirements around access control, auditability, retention, and data residency
Have worked across multiple cloud providers, or built infrastructure designed to be provider-agnostic
A product-minded approach to internal use …
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