About the Team
As AI training and deployments scale, the volume of data we need to monitor and understand is exploding. Our team uses Claude itself to make sense of this data. We own an integrated set of tools enabling Anthropic to ask open-ended questions, surface unexpected patterns, and maintain meaningful human oversight over massive datasets.
Our tools are widely adopted internally — powering ongoing enforcement, threat intelligence investigations, model audits, and more — and we’re looking for experienced engineers and researchers to both scale up existing applications and go zero-to-one on new ones.
About the Role
As a Research Engineer on our team, you'll design and build systems that let AI analyze large, unstructured datasets — think tens or hundreds of thousands of conversations or documents — and produce structured, trustworthy insights. You'll work across the full stack, from core analysis frameworks through user-facing apps and interfaces.
This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment.
Responsibilities:
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Design and implement AI-based monitoring systems for AI training and deployment
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Extend and improve core frameworks for processing large volumes of unstructured text
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Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions
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Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
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Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest
You May Be a Good Fit If You:
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Have 5+ years of software engineering experience, with meaningful exposure to ML systems
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Are excited about the problem of scaling human oversight of AI systems
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Are familiar with LLM application development (context engineering, evaluation, orchestration)
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Enjoy building tools that other people use — you care about UX, reliability, and documentation
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Can context-switch between deep infrastructure work and user-facing product thinking
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Thrive in collaborative, cross-functional environments
Strong Candidates May Also Have:
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Research experience in AI safety, alignment, or responsible deployment
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Practical experience with both data science and engineering, including developing and using large-scale data processing frameworks
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Experience with productionizing internal tools or building developer-facing platforms
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Background in building monitoring or observability systems
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Comfort with ambiguity — our team is small and growing, and you'll help define what we become
