About this role
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary: $500,000 — $850,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job l