<div><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><div> <div> <h2>About the role:</h2> </div> <div> <p>Agentic systems are the next frontier of usefulness for Claude.  Over the last year, we’ve seen rapid adoption of Claude-powered agentic systems in spaces like coding, research, customer support, network security, and more. We believe this is just the beginning, and we expect Claude to be handling much more complex tasks end-to-end or in cooperation with a human user as time goes on.  We have a team striving to make Claude an even more effective agent, focusing on planning, reliable execution over longer time horizon tasks, scaled tool use, memory, and inter-agent coordination. This team endeavors to maximize agent performance by solving challenges at whatever level is needed, whether it’s finetuning, agent infrastructure, or agent design best practices.</p> <p>Given that this is a nascent field, we ask that you share with us a project built on LLMs that showcases your skill at getting them to do complex tasks. Here are some example projects of interest: design of complex agents, quantitative experiments with prompting, constructing model benchmarks, synthetic data generation, model finetuning, or application of LLMs to a complex task. There is no preferred task; we just want to see what you can build. It’s fine if several people worked on it; simply share what part of it was your contribution. You can also include a short description of the process you used or any roadblocks you hit and how to deal with them, but this is not a requirement.</p> <h3> </h3> <h3><strong>Responsibilities:</strong></h3> <ul> <li>Finetune new capabilities into Claude that maximize Claude’s performance or ease of use on agentic tasks</li> <li>Ideate, develop, and compare the performance of different tools for agents  (eg memory, context compression, communication architectures for agents)</li> <li>Systematically discover and test prompt engineering best practices for agents</li> <li>Develop automated techniques for designing and evaluating agentic systems</li> <li>Assist with automated evaluation of Claude models and prompts across the training and product lifecycle</li> <li>Work with our product org to find solutions to our most vexing challenges applying agents to our products</li> <li>Help create and optimize data mixes for model training</li> <li>Help to create and maintain the infrastructure required for efficient prompt iteration and testing.</li> </ul> <h3><strong>You may be a good fit if you:</strong></h3> <ul> <li>Have 7+ years of ML and software engineering experience</li> <li>Have at least a high level familiarity with the architecture and operation of large language models</li> <li>Have extensive prior experience exploring and testing language model behavior</li> <li>Have spent time prompting and/or building products with language models</li> <li>Have good communication skills and an interest in working with other researchers on difficult tasks</li> <li>Have a passion for making powerful technology safe and societally beneficial</li> <li>Stay up-to-date and informed by taking an active interest in emerging research and industry trends.</li> <li>Enjoy pair programming (we love to pair!)</li> </ul> <h3><strong>Strong candidates may also have experience with:</strong></h3> <ul> <li>Developing complex agentic systems using LLMs</li> <li>Large-scale RL on language models</li> <li>Multi-agent systems</li> </ul> <h3><strong>Representative projects:</strong></h3> <ul> <li>Implementing and testing a novel retrieval, tool use, sub-agent, or memory architecture for Claude</li> <li>Finetuning Claude to maximize its performance using a particular set of agent tools (eg a read-write memory, or an inter-agent communication system)</li> <li>Building the prompting and model orchestration for a production application backed by a language model</li> <li>Building and testing an automatic prompt optimizer or automatic LLM-driven evaluation system for judging a prompt’s performance on a task.</li> <li>Building a scaled model evaluation framework driven by model-based evaluation techniques.</li> </ul> </div> </div><div><div><div><p>The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.</p></div><div>Annual Salary:</div><div><span>$340,000</span><span>—</span><span>$560,000 USD</span></div></div></div><div><h2><strong>Logistics</strong></h2> <p><strong>Education requirements: </strong>We require at least a Bachelors degree in a related field or equivalent experience.<strong><br /><br />Location-based hybrid policy:</strong> 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.</p> <p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we arent 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.</p> <p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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 youre interested in this work. We think AI systems like the ones were 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.</p> <h2><strong>How were different</strong></h2> <p>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. Were 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.</p> <p>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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p> <h2><strong>Come work with us!</strong></h2> <p>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. <strong>Guidance on Candidates AI Usage:</strong> Learn about <a href="https://www.anthropic.com/candidate-ai-guidance" target="_blank">our policy</a> for using AI in our application process</p></div>