Research Engineer, Model Evaluations

The shape of it

In the posting’s own words

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.

What it asks for · 5

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• results8×
• eval7×
• evaluations6×
• experience6×
• infrastructure6×
• researchers6×
• run6×
• training6×
• model5×
• build4×
• capability4×
• claude4×
• research4×
• dashboards3×
• data3×
• design3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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