Anthropic · San Francisco, CA
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.
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.
This is a foundational role: as one of the first engineers on this new team, 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 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. As model capabilities and usage grow, you'll also drive how we scale review through automation — building systems where Claude meaningfully extends what human reviewers can do, while keeping people in the loop where their judgment matters most.
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 signal back into model training. You'll partner closely with policy, operations, data science, legal, and privacy teams to ensure our enforcement systems are effective, accurate, and trustworthy.
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
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
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
8+ years of industry software engineering experience
Experience building trust and safety, integrity, fraud, or abuse-prevention tooling, or other systems supporting human review at scale
Experience designing systems under strict privacy, compliance, or data governance constraints, such as zero data retention environments
Experience integrating LLMs or agentic systems into operational workflows, or building human-in-the-loop automation — including using agentic coding tools (e.g., Claude Code) as a core part of your own development workflow
Experience building developer platforms or extensible tooling frameworks that other teams build on top of
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
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.
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
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! 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 encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit directly for confirmed position openings.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Confirmed on the employer’s own board 29m ago.
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