Engineering Manager, Safeguards Review Tooling
Anthropic is hiring an Engineering Manager, Safeguards Review Tooling in San Francisco, United States. It pays $405k-$485k a year and Level rates it ; you can apply on Anthropic.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Anthropic mentions visa sponsorship in this posting.
- Tools and skills the posting cites include LLM integration, AI safety and AI research.
- Level classifies it as a lead role.
- Level lists 680 other open roles at Anthropic.
AI in this role
Engineering manager leading review tooling and automation systems for AI safety investigations and enforcement.
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 an Engineering Manager to lead 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: 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.
You'll partner closely with policy, operations, data science, and legal teams to ensure our enforcement systems are effective, accurate, and trustworthy.
Key responsibilities
- Lead, grow, and develop a team of engineers building investigation, review, and enforcement tooling for both first-party and third-party platform surfaces
- Define the vision and roadmap for our review tooling platform, including analytics, privacy-compatible data access primitives, and a sandbox for rapidly developing new review interfaces
- Drive the team's strategy for scaling 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
- Ensure review tooling evolves alongside new privacy primitives and data retention commitments, so reviewers can do their work without compromising user trust
- Create clarity for the team and stakeholders in an ambiguous and evolving environment
- Take an inclusive, equitable approach to hiring and coaching top technical talent, and maintain a high-performing team
- Contribute to engineering-wide initiatives as a member of Anthropic's engineering management community
Minimum qualifications
- Experience managing software engineering teams, including hiring, coaching, and developing engineers
- 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
- 4+ years of management experience, 10+ 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
- 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
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:$405,000—$485,000 USDLogistics
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 anthropic.com/careers directly for confirmed position openings.
How we're different
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.
Come work with us!
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.
How we rate this
Engineering Manager, Safeguards Review Tooling at Anthropic rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How have you integrated a large language model into a production application?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a research question you investigated. What did you find?
- Tell me about a project where engineering management was part of your work. What did you do?
- Tell me about a project where safety was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: LLM Integration, AI Safety, AI Research, Engineering Management, and Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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