Staff+ Research Scientist, Multi-Agent

Multi-Agent systems are becoming an increasingly important part of how AI is deployed, whether via fast small-model subagents inside a product, or large groups of agents solving very large problems. Training Claude to be maximally effective and safe within large groups is a challenging new area of reinforcement learning, and represents a new axis for scaling test time compute.

We are looking for researchers who have experience training multi-agent systems at the largest scale and an appreciation for the incentives and mechanism design that come into play.

Responsibilities:

• Help create and optimize environments and data for model training that maximize Claude’s performance or ease of use on agentic tasks
• Ideate, develop, and compare the performance of different agent harness configurations (eg memory, context management, communication architectures for agents)
• Design and implement rigorous quantitative benchmarks for large scale agentic tasks
• Work with our product org to find solutions to our most vexing challenges in applying agents to our products