Research Attorney

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 As a member of the Research Legal team, you'll advise on legal topics across the full lifecycle of building and studying frontier AI models: data acquisition and use, training pipelines, model evaluation and launch, and alignment, interpretability, model welfare, societal impacts, and beneficial deployments research, as well as Responsible Scaling Policy work on biological risk. You'll be a subject matter expert on copyright, privacy, data use, and AI regulation (including the EU AI Act, U.S. state laws, and emerging global rules), and will work directly with researchers on questions that often have no settled answer. The ideal candidate will have navigated complex legal questions at the intersection of machine learning, data, and academic research in a fast-moving technical environment, with the judgment to work at the cutting edge of AI development while effectively solving issues, enabling innovation, and calibrating risk. This is a uniquely positioned role touching many different facets of AI, presenting the opportunity to be a leader in AI legal thought. Key responsibilities Advise research and data teams on data sourcing, licensing, partnerships, and use, and help shape Anthropic's data acquisition and use strategy as laws and markets evolve Help coordinate regulatory readiness and model launches, review model documentation and system cards, respond to regulatory inquiries, and advise on legal questions for pre-release and internal models Support the Responsible Scaling Policy as applied to biological and chemical risk, including capability evaluations, tiered access for biosecurity researchers, and dual-use research questions Counsel the Societal Impacts, Economic, Alignment, Interpretability, Model Welfare, and Beneficial Deployments teams on research design, data collection and sharing, external collaborations, program launches, and publication review Monitor global AI, privacy, copyright, and export-control developments and translate them into practical internal guidance and policy, working closely with Product, Policy, and other legal stakeholders Engage with academics, regulators, industry groups, and civil society to help develop norms and best practices for responsible AI development and data governance Minimum qualifications A JD and active membership in at least one U.S. state bar (California or New York preferred) Practical, creative problem-solving: the ability to apply legal requirements to fast-changing, deeply technical work, with an understanding that the goal is to enable responsible research and product development in a risk-calibrated environment Exceptional communication and analytical skills, technical fluency, and the ability to navigate ambiguous and uncharted legal territory, turning complex technical and legal analysis into advice people can act on Familiarity and comfort advising on the EU AI Act, U.S. legislation and AI frameworks, GDPR/CCPA, and comparable regimes globally, sometimes where no AI-specific legislation exists Interest in thinking deeply about the impact of AI on society and how to make humanity's transition to the AI age as beneficial as possible Sound judgment about when to be flexible and when to hold a line; comfort making high‑stakes calls in unsettled areas and outside your core expertise; and a low-ego, collaborative approach, doing the work needed rather than delegating it or being territorial Preferred qualifications At least 8 years of legal experience advising technology companies on copyright, privacy, data, AI regulation, and related product and policy issues, ideally including time in-house and work on AI/ML products or services Experience engaging directly with regulators or standards bodies Working technical literacy: enough familiarity with how models are trained, evaluated, and deployed to ask researchers and engineers the right questions and spot the legal issues #J-18808-Ljbffr