Research Engineer, Knowledge Team

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

Job summary

Anthropic is seeking an AI Engineer to advance research and production software for language models in a remote-friendly, US-based role with travel. You will design architectures, train models to interact with external data sources, and build robust evaluation methods to reveal system strengths and weaknesses.

You will collaborate with product and research teams to develop scalable retrieval, knowledge graphs, and agentic search capabilities, while delivering reliable code and aligning with

Qualifications

• Proven Python programming skills with reliable, maintainable code.
• Practical machine-learning research experience.
• Experience building software that uses large language models.
• Strong written and verbal communication with teamwork mindset.
• Willingness to contribute beyond a narrow job description.

Responsibilities

• Design and implement strategies for structuring information into knowledge bases.
• Fine-tune language models and apply reinforcement learning to improve interactions.
• Build evaluation datasets to expose failure modes in external-data use.
• Design and assess advanced agentic search capabilities.
• Collaborate with product and research teams to deliver capabilities.
• Develop scalable retrieval systems involving search, indexing, ranking, and knowledge graphs.

Skills

Python

Machine learning

Information retrieval

Communication

Pair programming

Job description

AI Engineer Remote-friendly within a US-based role with required travel and possible 25% office attendance

Job details

Remote-friendly within a US-based role with required travel and possible 25% office attendance Eligibility

Staff Experience

Not specified Employment

About this role

Role overview

This research engineering role explores new ways for language models to organize, retrieve, and use information from external data sources. You will design information architectures, train models to interact with them, and develop evaluations that reveal where those systems succeed or fail. The work combines machine learning research, information retrieval, agent design, and production-oriented software engineering.

Responsibilities

• Design and implement new strategies for structuring information and knowledge bases for model use.
• Fine-tune language models and apply reinforcement-learning methods to improve their interaction with those structures.
• Build challenging evaluation datasets that expose failure modes in external-data use.
• Design and assess advanced agentic search capabilities.
• Prototype and deliver new capabilities in collaboration with product and research teams.
• Develop or contribute to scalable retrieval systems involving search, indexing, ranking, query understanding, knowledge graphs, RAG, and distributed data processing.

Requirements

• Very strong Python programming skills, including the ability to produce reliable, maintainable code quickly.
• Practical machine-learning research experience.
• Experience building software that uses large language models.
• Results-oriented judgment, flexibility, and willingness to contribute beyond a narrowly defined job description.
• Clear written and verbal communication, with an interest in pair programming and close collaboration.
• Awareness of the broader societal implications of machine-learning systems.

Nice to have

Experience building complex agentic systems, scalable distributed information-retrieval infrastructure, or products that turn research prototypes into usable capabilities is especially relevant.

Benefits and work setup

The role is remote-friendly but requires travel and may be based from offices in San Francisco, Seattle, or New York City. The general expectation is that staff spend at least 25% of their time in an office, with some roles requiring more. Visa sponsorship may be available depending on the role and candidate. The listed annual salary range is $350,000–$850,000 USD.