The Knowledge Work team at Anthropic builds training environments and evaluations that make Claude effective at real‑world professional workflows. As a Research Engineer on Knowledge, you will design and run experiments to improve Claude's search, retrieval, and reasoning capabilities at scale, working across environment design, data curation, RL training, evaluation, and supporting infrastructure. You will partner closely with researchers and other RL teams to ship capabilities that directly influence Claude’s behavior, contributing to shared infrastructure, tooling, and operational observability.
Research Engineer, Knowledge Foundations at Anthropic
Hybrid - San Francisco, CA, United States
More jobs at AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- Are 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
- Have 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
- 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
- Are comfortable being the long‑term, context‑rich owner of a system and its operational health
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
Technologies
PythonMachine LearningData PipelinesModel TrainingEvaluationReinforcement LearningDistributed SystemsLarge Language Models
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