Anthropic

Staff+ Software Engineer, Safeguards Review Tooling

San Francisco, CA · Staff+

H1B sponsorship available$320k-$485kDetected 4 days ago

Backend DevelopmentFull-Stack DevelopmentCloud PlatformsPlatform EngineeringData ScienceNLPLLMsAgentic AILogisticsResearchCommunication

About the role

The Safeguards team is responsible for ensuring our models and products are developed and deployed safely.
That platform includes analytics 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.

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
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
A technical background in full-stack or platform engineering, with the ability to engage deeply in architecture and design discussions
Experience working cross-functionally with non-engineering partners such as operations, policy, or legal teams
Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

Experience shipping internal tools or platforms with demanding operational users, and a track record of improving their workflows measurably
Excellent communication skills, including the ability to explain technical tradeoffs to non-technical stakeholders
Years of experience required will correlate with the internal job level requirements for the position
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

8+ years of industry software engineering experience
Experience designing systems under strict privacy, compliance, or data governance constraints, such as zero data retention environments
Experience supporting enforcement or moderation systems across multiple product surfaces, including enterprise or cloud platform contexts
A product-minded approach to internal users: you work directly with the people using your tools, watch where they struggle, and fix it

$320,000 - $485,000 USD
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.

Build in the guardrails that sensitive internal tools require: granular permissions, audit trails, data-access controls, and reviewer wellbeing features such as content obfuscation and exposure limits
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 education: Bachelor's degree or an equivalent combination of education, training, and/or experience

Anthropic's mission is to create reliable, interpretable, and steerable AI systems.

However, we aren't able to successfully sponsor visas for every role and every candidate.
But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We do sponsor visas!

This listing is sourced directly from Anthropic's careers page and normalized into a canonical job model.