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Public Policy, Public Affairs Strategy
Public Policy, Public Affairs StrategyAnthropic • San Francisco, CA, US
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Public Policy, Public Affairs Strategy

Public Policy, Public Affairs Strategy

Anthropic • San Francisco, CA, US
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  • [job_card.full_time]
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Public Policy, Public Affairs Strategy

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

We are seeking a new member of our Central Policy team to help build the engagement infrastructure that enables Anthropic's policy experts to communicate effectively with internal and external audiences. This role will be instrumental in developing and maintaining the policy collateral, processes, and campaigns that strengthen Anthropic's policy influence in Washington and beyond.

In This Role, You Will :

Draft and maintain a comprehensive set of internal and external policy collateral including one-page position summaries, technical FAQs, factsheets, talking points, and other written materials

Develop and execute processes to build and maintain organizational consensus on technical policy positions, working with subject matter experts across federal affairs, state affairs, national security, and international affairs to synthesize viewpoints and refine language

Serve as a strategic advisor and partner to federal, state, and international affairs teams to develop and execute bespoke public affairs strategies

Anticipate emerging policy issues and proactively work with internal experts to develop Anthropic's perspective and supporting materials before external pressure emerges

Maintain a curated database and organizational system for policy collateral that tracks versions, intended audiences, use cases, strategic relevance, and access

Manage campaigns or programs including project planning, stakeholder coordination, process management, and execution

Conduct policy research and contribute to the development of policy proposals that help society make a safe transition to powerful AI systems

Work closely with our policy communications team to coordinate messaging across internal and external materials and ensure consistency in Anthropic's policy brand and voice

Develop and manage internal workflows and processes that help policy teams operate efficiently, including collateral request processes, review cycles, and stakeholder engagement protocols

Establish metrics to track the use, reach, and impact of policy collateral and campaigns, and regularly report on progress

You May Be A Good Fit If You :

Have 10+ years of professional experience in corporate public affairs or policy communications in the private sector, with demonstrated success bridging the gap between policy analysis and outcomes

Are an excellent writer who can synthesize complex technical material into clear, concise language tailored to different audiences

Have extensive experience conducting policy research and developing policy proposals

Have demonstrated experience driving cross-functional projects and building consensus in fast-moving, ambiguous environments

Have a track record of building processes and initiatives from 0 ? 1, including defining workflows, establishing best practices, and scaling operations

Excel at managing multiple projects simultaneously with strong organizational skills and time management

Can work independently, take ownership of problems, and proactively anticipate needs

Are scrappy and hard-working, but also humble, genuinely collaborative, and a strong team player

Are passionate about AI policy and align with Anthropic's safety mission and commitment to responsible AI development

Hold at least a Bachelor's degree in a related field

Logistics

Education requirements : We require at least a Bachelor's degree in a related field or equivalent experience. 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.

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.

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