Research Engineer, Knowledge Foundations

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.

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

Qualifications

• A highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
• Experience designing, running, and analyzing ML experiments
• Ability to work across the stack — from data pipelines to model training to evaluation
• 5+ years of experience operating ML or distributed systems at scale
• Comfort working with ambiguity and choosing the most impactful problem to tackle next
• Clear written and verbal communication, especially when collaborating across time zones
• Find genuine satisfaction and impact in making existing critical systems dependable

Preferred Qualifications

• Hands-on experience training, fine-tuning, or doing RL on large language models
• Experience building evaluations for LLMs, particularly in open-ended or knowledge-intensive domains
• Prior work in a research-heavy environment such as a frontier AI lab, quant research firm, or domain-focused AI startup
• Published research on LLMs, RL, retrieval, or related areas
• Experience with distributed training systems