Staff Research Engineer, Multi-Agent Scaling

Anthropic · San Francisco, CA | New York City, NY | Seattle, WA · AI Research & Engineering · listed October 2, 2026

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The shape of it

Seniority
Staff
Where
Hybrid
Stated pay
$500,000 – $850,000 USD
Requirements listed
8
Length
1,164 words

In the posting’s own words

This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly.

What it asks for · 8

  • Have significant software engineering, ML or research engineering experience
  • Have 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 think twice before trusting a number
  • Can work from a vague question rather than a spec
  • Are results-oriented, with a bias towards flexibility and impact
  • Have clear written and verbal communication
  • Care about the societal impacts of your work

Also a plus

  • Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure
  • Built evaluations, benchmarks or harnesses for LLMs or agents
  • Experience building complex agentic systems that use LLMs
  • Experience with scaling laws or other large-scale empirical research
  • A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems
  • Formal certifications or education credentials
  • Academic research experience or publication history
  • Prior experience with multi-agent systems or reinforcement learning

What the job covers

  • Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show
  • Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them
  • Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale
  • Design evaluations for long-horizon problems, and keep their results trustworthy
  • Build the tooling and metrics that let researchers see what a large agent team is doing and why
  • Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly

Tools and skills named

Models & research
  • Evaluations3×
  • LLM2×
  • Inference
  • Machine learning
  • Reinforcement learning
Cloud & infra
  • Distributed systems
Data
  • Statistics

Words the posting leans on

  • agent13×
  • research9×
  • systems8×
  • large7×
  • run7×
  • build6×
  • experience6×
  • evaluations5×
  • scale5×
  • design4×
  • engineering4×
  • experiments4×
  • large agent4×
  • problem4×
  • grow3×
  • large-scale3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched October 2, 2026 and last changed by Anthropic on October 2, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.