Frontier Counsel
AnthropicSan Francisco, CA | New York City, NYPosted 7 April 2026
Tech Stack
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
As a member of the Frontier Counsel function, you’ll work on the cutting edge of AI, serving as a trusted legal advisor to Anthropic’s Research organization and Compute team, providing strategic guidance on data acquisition, risk mitigation, rapidly evolving regulatory regimes, and a variety of other complex legal issues at the intersection of AI development and responsible innovation. You’ll be a subject matter expert on copyright, privacy, and related issues pertaining to data used to develop frontier AI models, while also counseling on compute infrastructure considerations including data localization and cross-border data flows. You may also support our Alignment, Frontier Red-Teaming, Policy, and Societal Impacts teams to ensure safe, responsible AI development. The ideal candidate will have experience navigating complex legal challenges globally in a fast-moving technical environment, as well as translating legal requirements into creative and practical solutions for pioneering AI research and development.
Responsibilities:
Advise Research teams on permissible data usage and partner with them on data sourcing strategy, maintaining and evolving our data acquisition frameworks, and supporting Research and Data Operations requests
Counsel the Compute team on relevant legal considerations including data localization requirements, cross-border data transfers, privacy implications, and copyright considerations for compute infrastructure, partnering with internal subject matter experts as needed
Monitor regulatory developments related to AI training data and compute operations, adapting internal policies and practices proactively to ensure compliance across jurisdictions
Translate data and model development implications between research teams and Legal Policy stakeholders, serving as a bridge between technical and legal functions
Engage with academics, industry groups, and other stakeholders to develop norms and best practices around data governance for AI development
Support Anthropic’s Alignment and Frontier Red-Teaming teams to advance their research on AI safety and model vulnerabilities, including advising on vendor engagements, external collaborations, and data usage
You may be a good fit if you have:
A JD and active membership in at least one U.S. state bar (California preferred)
At least 8 years of relevant legal experience, with meaningful experience advising on data localization and cross-border data transfer frameworks, copyright, privacy, data, and related issues for technology companies or AI/ML products (in-house experience preferred)
A creative, practical problem solver attitude with a passion and capacity for translating and applying legal requirements to technical concepts related to model development. You understand the importance of enabling innovation while developing responsibly and mitigating risks
Experience advising on legal issues for AI products or services and academic research, with knowledge of the machine learning development lifecycle
Excellent written communication skills, with the ability to draft clear policies, whitepapers, and other content. You enjoy engaging with stakeholders to evangelize best practices
An understanding of how to achieve the right balance between the organization’s mission and goals, knowing when to be flexible and when to draw a hard line
An enjoyment of wearing many hats in a fast-growing startup environment and are comfortable operating outside your areas of expertise and in uncharted legal territory
A “doer” mentality and are willing to roll up your sleeves to get things done. You’re a team player who doe ... (truncated, view full listing at source)
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