About the role
How do we know our safety systems actually catch misuse? Anthropic increasingly uses AI to investigate potential misuse of Claude — analyzing real-world traffic to surface bad actors, policy violations, and emerging threats. Its findings inform enforcement actions and model launch decisions, which means we need rigorous, trustworthy answers to questions like: Does the monitoring agent catch what it should? Where does it fail? Does it stay reliable as adversaries adapt, as models improve, and as the agent itself changes?
This role builds the evaluation infrastructure that answers those questions. You'll sit at the intersection of applied ML research and engineering — designing experiments to measure how well an investigative agent performs across harm areas, building datasets that represent real abuse rather than synthetic benchmarks, and shipping those methods into pipelines that gate every change to the system. Your work directly determines how much trust Anthropic can place in its automated abuse detection, and where we invest to make it better.
Key responsibilities
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Build and own the evaluation harness for an agentic investigation system — defining metrics, test cases and grading approaches for a complex long horizon agent
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Construct high-quality eval datasets representing real-world misuse across harm areas (e.g., cyber attacks, bio weapons, influence operations), drawing from real traffic patterns and synthetic generation
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Measure agent performance end-to-end (detection precision/recall, investigation quality, robustness) and drive hill-climbing on the hardest harm areas
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Analyze coverage to identify measurement gaps, and evolve evals so they remain unsaturated and high-signal as agent capabilities advance
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Productionize successful research into regression and release pipelines that run on every agent change, prompt update, and underlying model upgrade
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Build tooling that enables policy experts to author, run, and iterate on evaluations without engineering support
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Construct RL environments to improve Claude’s safety investigation capabilities.
Minimum qualifications
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Proficiency in Python and comfort working across the stack
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Experience building and maintaining data pipelines
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Experience working with LLMs and a working understanding of their capabilities and failure modes — especially agentic systems with tool use and multi-step reasoning
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Strong data analysis skills — you can draw reliable insights from large datasets
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Ability to move fluidly between research prototyping and production-quality code
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Ability to translate ambiguous problems into concrete, testable experiments
Preferred qualifications
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6+ years of industry software engineering experience
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Expertise in building or contributing to agent evaluation frameworks, benchmarks, or automated grading systems
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Extensive experience in trust and safety, content moderation, or abuse detection systems
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Experience in red teaming, adversarial testing, or jailbreak research on AI systems
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Experience with synthetic data generation or data augmentation
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Experience with distributed systems or large-scale data processing
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Experience with prompt engineering or building LLM-powered applications
