About the job
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.
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
Qualifications
Minimum
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
Hands-on experience using large language models such as Claude, including prompting, sampling, and scaffolding
Background in data visualization and a track record of building dashboards people actually trust and use
Experience developing robust evaluation metrics for language models
Experience with observability, monitoring, or experiment-tracking systems
Background in statistics and experimental design
Experience with large-scale dataset sourcing, curation, and processing
Experience running or supporting ML training infrastructure
A bias toward picking up slack and operating flexibly across team boundaries
Enjoy pair programming — we love to pair