Research Engineer, Knowledge Foundations

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

The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows — searching, analyzing, and creating across the tools and documents knowledge workers use every day. As that work scales, the systems behind it need to be as rigorous as the research itself.

As a Research Engineer on Knowledge, you'll design and run experiments that improve how Claude searches, retrieves, and reasons over information at scale. The work spans environment design, data curation, RL training, evaluation, and the infrastructure that supports it all. You'll move fluidly between these depending on what's blocking progress. You'll partner closely with researchers and other RL teams to ship capabilities that show up directly in Claude's behavior.

As our training and evaluations continue to scale, we see a strong synergy between the capabilities our models learn, the tools we build for them to use, and the tools we build for ourselves to understand it all. We own the science behind superhuman epistemics and we ensure the quality of the stack that drives it. We understand that real ownership and impact comes as much through hardening and iterating on environments as it does creating new ones.

Responsibilities

• Design, build, and iterate on training environments and data pipelines that improve Claude's ability to reason over knowledge-intensive tasks
• Run experiments end-to-end: form a hypothesis, build the infrastructure, train models, analyze results, and decide what to try next
• Develop evaluations that meaningfully capture progress on search, retrieval, and reasoning quality
• Identify failure modes in current model behavior and translate them into concrete training signals
• Collaborate closely with researchers across RL Data, post-training, and product teams to align on priorities and ship improvements
• Contribute to shared infrastructure and tooling that compounds the team's velocity over time
• Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
• Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation

You may be a good fit if you

• Are a highly experienced Python engineer who ships reliable, well-ins