Research Engineer, Model Evaluations

Anthropic; United States; Remote; USD 120,000 - 230,000; Full time

Job summary

Anthropic is seeking Research Engineers to design evaluations that quantify Claude's capabilities, reasoning, safety properties, and alignment with leadership expectations.

You will build scalable eval infrastructure, run experiments across live checkpoints, and present clear results to researchers and decision-makers to push Anthropic toward leadership in well-characterized AI systems.

Qualifications

• Strong Python programming skills, production or research infra experience.
• Experience building or operating distributed systems and data pipelines.
• Clear written and verbal communication to explain technical results to non-specialists or leadership.

Responsibilities

• Design and run evaluations of Claude's capabilities, safety, and behavior.
• Build and harden a scalable evaluation platform used during training and testing.
• Own dashboards and visualizations to monitor model health and evaluation outcomes.
• Debug anomalous eval results during training and communicate findings clearly under time pressure.
• Collaborate with research teams across the full lifecycle of new capabilities, from measurement to interpretation.

Skills

Python

Distributed Systems

Communication

Job description

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

We're looking for Research Engineers to build the evaluations that tell us — and the world — what Claude can actually do. Your work will turn ambiguous notions of "intelligence" into clear, defensible metrics that researchers, leadership, and the public can rely on.

You will design and implement evaluations across the full spectrum of Claude's capabilities and personality, and build the infrastructure that runs them reliably at scale. You'll partner closely with researchers throughout the lifecycle of a new capability — from defining what to measure, to running the eval against live training checkpoints, to interpreting the results. The goal is to make Anthropic the leader in extremely well-characterized AI systems, with performance that is exhaustively measured and validated across the tasks that matter.

Key responsibilities

• Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers
• Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs
• Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss
• Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure
• Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations
• Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses
• Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks
• Communicate evaluations and their results to internal stakeholders and, where appropriate, external audiences

Minimum qualifications

• Strong Python programming skills, including production or research infrastructure
• Experience building or operating distributed systems, data pipelines, or other infrastructure that needs to be reliable at scale
• Clear written and verbal communication, especially when explaining technical results to non-specialists
• Comfort operating in an on-call or production-support capacity when training runs are live
• Care about the societal impacts of your work and an interest in steering powerful AI to be safe and beneficial

Preferred qualifications

• Hands-on experience using large