Staff Research Engineer: Scalable Agent Systems

EngineersOfAI; New York (NY); On-site; USD 170,000 - 210,000; Full time

Job summary

Anthropic is seeking a generalist engineer at the boundary of research and engineering. You will design and run large experiments, build the systems they run on, and dig into surprising results. The role involves moving from vague questions to running experiments quickly and scaling to very large agent teams.

This position emphasizes rigorous analysis of data, scaling performance and efficiency with team size and task horizon, and building trustworthy evaluations and tooling for researchers.

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.
• Genuinely enjoy both research and engineering work.
• Think quantitatively about complex systems and verify numbers before trust.

Responsibilities

• Design, run and interpret large-scale experiments on agent teams, reasoning about data.
• Investigate performance and efficiency as team size, compute and horizon grow, identify bottlenecks.
• Build and scale systems that run large agent teams reliably and debug scale-related failures.
• Design evaluations for long-horizon problems and ensure results are trustworthy.
• Develop tooling and metrics to help researchers see what a large agent team is doing and why.
• Collaborate with research teams to run their experiments on the platform and communicate findings.

Skills

Software engineering

ML

Research engineering

Job description

Anthropic is seeking a generalist engineer at the boundary of research and engineering. You will design and run large experiments, build the systems they run on, and dig into surprising results. The role involves moving from vague questions to running experiments quickly and scaling to very large agent teams.

This position emphasizes rigorous analysis of data, scaling performance and efficiency with team size and task horizon, and building trustworthy evaluations and tooling for researchers.