Software Engineer, Safeguards Evals San Francisco, CA | New York City, NY

Overview 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 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 Build and own the evaluation harness for an agentic investigation system — defining metrics, test cases and grading approaches for a complex long‑horizon agent. 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. Measure agent performance end‑to‑end (detection precision/recall, investigation quality, robustness) and drive hill‑climbing on the hardest harm areas. Analyze coverage to identify measurement gaps, and evolve evals so they remain unsaturated and high‑signal as agent capabilities advance. Productionize successful research into regression and release pipelines that run on every agent change, prompt update, and underlying model upgrade. Build tooling that enables policy experts to author, run, and iterate on evaluations without engineering support. Construct RL environments to improve Claude’s safety investigation capabilities. Proficiency in Python and comfort working across the stack. Experience building and maintaining data pipelines. Experience working with LLMs and a working understanding of their capabilities and failure modes — especially agentic systems with tool use and multi‑step reasoning. Strong data analysis skills — you can draw reliable insights from large datasets. Ability to move fluidly between research prototyping and production‑quality code. Ability to translate ambiguous problems into concrete, testable experiments. Qualifications Experience building and maintaining data pipelines. Proficiency in Python and the ability to work across the stack. Strong data analysis skills. Ability to move fluidly between research prototyping and production‑quality code. Experience working with LLMs and an understanding of their capabilities and failure modes. Ability to translate ambiguous problems into concrete, testable experiments. Preferred Qualifications 6+ years of industry software engineering experience. Expertise in building or contributing to agent evaluation frameworks, benchmarks, or automated grading systems. Extensive experience in trust and safety, content moderation, or abuse detection systems. Experience in red teaming, adversarial testing, or jailbreak research on AI systems. Experience with synthetic data generation or data augmentation. Experience with distributed systems or large‑scale data processing. Experience with prompt engineering or building LLM‑powered applications. Compensation

$320,000 - $485,000 USD

Benefits 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. Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Field of study should be relevant to the role as demonstrated through coursework, training, or professional experience. Minimum years of experience correlate with internal job level requirements. Location‑based hybrid policy: Staff are expected to be in one of our offices at least 25% of the time. Visa sponsorship: We sponsor visas for qualified candidates as part of our hiring process. EEO Statement As set forth in Anthropic’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. #J-18808-Ljbffr Anthropic