Staff+ Research Scientist, Multi-Agent
- Location
- San Francisco, CA, United States
- Track
- AI Researcher
- Level
- Staff
- Posted
- April 22, 2024
- Source
- Greenhouse
Job description
About the role
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
You may be a good fit if you
- Have experience with large-scale RL on language models
- Have experience training multi-agent systems
- Enjoy going deeply into the roots of a problem and understanding its foundations, rather than its surface.
- Have good communication skills and an interest in working with other researchers on difficult tasks
- Have a passion for making powerful technology safe and societally beneficial
- Are excited for a mission-driven org with fast-paced, impactful work
Representative projects
- Design and build reinforcement learning environments to train groups of Claudes how to solve problems together efficiently
- Design and build agent affordances that unlock new capabilities and scales of agents, while keeping the Bitter Lesson in mind
- Design and build a novel eval that measures how large teams of agents interact in groups to solve problems
Annual Salary
$500,000 — $850,000 USD