Research Engineer, Knowledge Team

Anthropic; United States; Hybrid; USD 350,000 - 850,000; Full time

Job summary

Anthropic is seeking Research Engineers to redesign how Claude interacts with external data sources, designing new architectures for information organization and training language models to optimally use those structures.

You will work with a team of researchers and engineers to advance safe, reliable AI, focusing on data architectures, RL for LLMs, and evaluating agentic search capabilities to enhance AI systems.

Qualifications

• Bachelor's degree in a field relevant to the role.
• Very experienced Python programmer with high-quality code.
• Experience in machine learning research and building LLM-based systems.
• Experience using Large Language Models such as Claude and integrating them into software.

Responsibilities

• Design new information architecture strategies from scratch.
• Perform finetuning and reinforcement learning to teach language models how to interact with new information architectures.
• Build hard knowledge base eval sets to identify failure modes of language models with external data.
• Design and evaluate advanced agentic search capabilities.

Skills

Python programming

Machine learning research

LLMs (Claude)

Communication skills

Collaborative/teamwork

Education

Bachelor's degree

Job description

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

• 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

• 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.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:$350,000—$850,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study:A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our