Staff Research Engineer — Large-Agent Systems & Experiments

Anthropic; San Francisco (CA); Hybrid; USD 500,000 - 850,000; Full time

Benefits offered by this job

Equity donation matching

Generous vacation

Parental leave

Flexible working hours

Office space

Job summary

Anthropic is seeking a generalist researcher-engineer to design and run large experiments on agent teams. You will build the systems they run on, scale them, and investigate surprising results. The role blends research with engineering and requires hands-on experimentation from vague questions to running code.

You will work across Anthropic’s teams to enable experiments, measure performance, and ensure trustworthy evaluations at scale, contributing to safe and beneficial AI research.

Qualifications

• Significant software engineering, ML or research engineering experience.
• Owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project.
• Clear written and verbal communication.

Responsibilities

• Design, run and interpret large-scale experiments on agent teams.
• Investigate performance and efficiency changes as team size, compute and horizon grow.
• Build and scale systems that run very large agent teams reliably.
• Design evaluations for long-horizon problems and maintain trustworthiness.
• Build tooling and metrics to observe large agent team activity.
• Collaborate with research teams to run experiments on the platform and communicate findings.

Education

Bachelor’s degree

Tools

Distributed systems

Large-scale experiments

Job description

Anthropic is seeking a generalist researcher-engineer to design and run large experiments on agent teams. You will build the systems they run on, scale them, and investigate surprising results. The role blends research with engineering and requires hands-on experimentation from vague questions to running code.

You will work across Anthropic’s teams to enable experiments, measure performance, and ensure trustworthy evaluations at scale, contributing to safe and beneficial AI research.