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YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_20e650c2-d9c"},"title":"Research Scientist, Interpretability","description":"<p><strong>About the role:</strong></p>\n<p>When you see what modern language models are capable of, do you wonder, &#39;How do these things work? How can we trust them?&#39;</p>\n<p>The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. We’re looking for researchers and engineers to join our efforts.</p>\n<p>People mean many different things by &#39;interpretability&#39;. We&#39;re focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do &#39;biology&#39; or &#39;neuroscience&#39; of neural networks using “microscopes” we build, or as treating neural networks as binary computer programs we&#39;re trying to &#39;reverse engineer&#39;.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights</li>\n</ul>\n<ul>\n<li>Design and run robust experiments, both quickly in toy scenarios and at scale in large models</li>\n</ul>\n<ul>\n<li>Create and analyse new interpretability features and circuits to better understand how models work.</li>\n</ul>\n<ul>\n<li>Build infrastructure for running experiments and visualising results</li>\n</ul>\n<ul>\n<li>Work with colleagues to communicate results internally and publicly</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have a strong track record of scientific research (in any field), and have done _some_ work on Interpretability</li>\n</ul>\n<ul>\n<li>Enjoy team science – working collaboratively to make big discoveries</li>\n</ul>\n<ul>\n<li>Are comfortable with messy experimental science. We&#39;re inventing the field as we work, and the first textbook is years away</li>\n</ul>\n<ul>\n<li>You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results</li>\n</ul>\n<ul>\n<li>You can clearly articulate and discuss the motivations behind your work, and teach us about what you&#39;ve learned. You like writing up and communicating your results, even when they&#39;re null</li>\n</ul>\n<p><strong>Role Specific Location Policy:</strong></p>\n<ul>\n<li>This role is based in San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>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>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<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.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_20e650c2-d9c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4980427008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["Python","Mechanistic Interpretability","Neural Networks","Reverse Engineering","Experimental Science"],"x-skills-preferred":["Research","Engineering","Team Science","Communication"],"datePosted":"2026-03-08T13:48:39.765Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Mechanistic Interpretability, Neural Networks, Reverse Engineering, Experimental Science, Research, Engineering, Team Science, Communication","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}}]}