Staff+ Research Scientist, Multi-Agent

Anthropic; New York (NY); On-site; USD 500,000 - 850,000; Full time

Job summary

Anthropic Limited in New York, USA, seeks researchers to advance multi-agent reinforcement learning at scale, specializing in training Claude within large groups. You will design environments and agent harness configurations to optimize performance and safety.

Collaborating with product teams, you will develop rigorous benchmarks for agentic tasks and contribute to deploying powerful AI responsibly.

Qualifications

• Experience with large-scale RL on language models.
• Experience training multi-agent systems.
• Strong foundation and problem-solving approach.
• Strong communication skills.
• Passion for making powerful technology safe and societally beneficial.
• Mission-driven with fast-paced, impactful work.

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

Skills

Large-scale RL

Multi-agent training

Communication skills

Foundations & problem-solving

Education

Bachelor’s degree

Job description

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

Annual Salary

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.

$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 level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long‑term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.