Staff+ Research Scientist, Multi-Agent

Location: San Francisco, CA | New York City, NY | Seattle, WA 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 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. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. 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. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.