Research Engineer, Knowledge Team

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:

We are looking for Research Engineers to help us redesign how Claude interacts with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of LLMs! Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures.

Responsibilities:

Designing and implementing from scratch new information architecture strategies

Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures

Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data

Designing and evaluating advanced agentic search capabilities.

You may be a good fit if you:

Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using

Have good machine learning research experience

Have experience developing software that utilizes Large Language Models such as Claude

Are results-oriented, with a bias towards flexibility and impact

Pick up slack, even if it goes outside your job description

Enjoy pair programming (we love to pair!)

Want to partner with world-class ML researchers to develop new LLM capabilities

Care about the societal impacts of your work

Have clear written and verbal communication

Strong candidates will also have experience with:

Collaborating with product teams to quickly prototype and deliver innovative solutions

Building complex agentic systems that utilize LLMs

Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing

The annual compensation range for this role is listed below.