Staff Research Engineer, Multi-Agent Scaling

Anthropic•Seattle, WA

8h•$500,000 - $850,000•Hybrid

About The Position

Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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