Research Engineer, Knowledge Team at Anthropic

Role overview

Anthropic seeks a Research Engineer to redesign how Claude uses external data and knowledge bases. The role explores information architectures built for language models and trains models to use them effectively.

Responsibilities

• Design and implement new ways to organize information for language-model use.
• Fine-tune and use reinforcement learning to improve model interaction with those information structures.
• Build demanding knowledge-base evaluation sets to reveal failures with external data.
• Design and evaluate advanced agentic search capabilities.

Qualifications

• Strong Python programming skills and experience producing reliable software.
• Machine-learning research experience and work with applications built on large language models.
• Ability to collaborate with ML researchers and communicate clearly.
• Experience with agent systems, information retrieval, RAG, indexing, ranking or distributed data processing is especially relevant.

Technology in this role

• Python: Strong Python programming skills and experience producing reliable .
• LLM: • Want to partner with world-class ML researchers to develop new LLM .
• large language models: Machine-learning research experience and work with applications built on large language .
• RAG: Experience with agent systems, information retrieval, RAG, indexing, ranking or distributed data processing is especially .
• CAN: • Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Research Engineer, Knowledge Team position at Anthropic is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “Anthropic seeks a Research Engineer to redesign how Claude uses external data and knowledge bases. The role explores information architectures built for language models and trains models to use them effectively.” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the technical area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Design and implement new ways to organize information for language-model use.”; “Fine-tune and use reinforcement learning to improve model interaction with those information structures.”; “Build demanding knowledge-base evaluation sets to reveal failures with external data.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Strong Python programming skills and experience producing reliable software.”; “Machine-learning research experience and work with applications built on large language models.”; “Ability to collaborate with ML researchers and communicate clearly.”. Mark each requirement as demonstrated, adjacent experience or a genuine gap, then use a concise example for the first two categories. A strong example explains what you personally owned, how you made trade-offs, how you validated the result and what you learned. If the posting specifies years of experience, language ability, certifications, location or eligibility, address those facts plainly in your CV or application rather than relying on the employer to infer them. Do not claim a qualification from exposure alone. For a requirement you do not meet, describe relevant transferable experience accurately and ask the recruiter whether it is mandatory.

Technology and working evidence. The technology terms associated with this listing are Python. Treat this as a set of posting-derived signals, not a guarantee that every tool is used daily or that the list is complete. For each term relevant to your experience, prepare one concrete example: the system or workflow, the part you owned, the alternatives considered, and how you tested reliability, security, performance or usability as appropriate. If you have not used a named tool, explain the closest related system you have worked with and how you would ramp up; do not relabel that experience as direct production use. If the source describes technologies beyond this list, prioritize its exact wording.

Evidence to put in the application. Tailor the first half of your CV to the named work instead of using a generic summary. Lead with two or three outcomes that.

Benefits

• The posting lists vacation and parental leave, flexible working hours and optional equity donation matching.
• Anthropic states that staff generally spend at least 25% of their time in an office; some roles require more. Check the posting for this role’s location and travel conditions.

About Anthropic

Anthropic develops AI systems with a focus on reliability, interpretability, steerability and safety.

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