Tabletop Exercise Lead, The Anthropic Institute

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 Anthropic Institute
The Anthropic Institute (TAI) is a function within Anthropic that works to advance the public conversation around AI.
The Institute is comprised of research teams tackling some of AI's most consequential societal challenges: the Frontier Red Team, which stress tests our systems against catastrophic risks; Societal Impacts, which studies how Claude is used by millions of people; Economic Research, which tracks AI's impact on people's jobs and the larger economy; the Rule of Law team, which studies AI's impacts to democracy; and more.
The Institute's mission is to reduce information asymmetries between Anthropic and the outside world, name and answer the hard questions implied by powerful AI systems, and publish these findings so that policymakers, researchers, technology leaders, and the public can reckon with what's here and what's to come.
About the role
The role. You will build and lead the Institute’s tabletop exercise program and the team that runs it. Based on trends visible in the exponential and in consultation with technical experts, you will set the research agenda: which questions the exercises should answer, which scenarios to run, with which audiences, and what each run is meant to test. You will own the methods that make results comparable across runs and turn them into findings people outside the room can use. You will hire and manage a team plus a bench of facilitators, and you will be the program’s public voice.
Why tabletop exercises? If RSI arrives, the hardest problems will be about making decisions under uncertainty. The people making them may not trust each other, and the authorities they work under may not be fit-for-purpose. A tabletop exercise (TTX) allows the people who would face that situation practice it before it is real. It also shows us what tools and information they reach for, including what they would need that does not exist yet.
Tabletop exercises are also a research method. You can run the same scenario many times, with different groups of people (and with many more AI agents playing in silico), and you start to see the distribution of decisions people make. This is one of the few ways to generate empirical evidence about the future, i.e., how people and institutions will respond to situations that have not happened yet.
Key responsibilities
Set the program’s research agenda: the questions exercises should answer, the scenarios to run, and the audience for each
Design exercises as empirical studies: define what each run tests, standardize scenarios and data capture so results are comparable, and use repeated play to map the range of outcomes
Build the program’s measurement: pre- and post-exercise assessments, decision logs, transcript analysis and after-action reports
Turn results into findings: publish on what the exercises show, and brief senior people inside and outside Anthropic on the gaps they expose
Hire, manage and develop a team and a bench of facilitators
Make the program scalable: maintain a library of tested scenarios that the team reuses most of the time, refreshed on a regular cadence so they stay timely
Build a facilitator kit ("TTX in a box") so other internal teams or external partners can run exercises to the same standard
Create and direct the tools behind the exercises, including Claude-driven simulations that generate a live outside-world feed during play
Minimum qualifications
Have designed and run tabletop exercises, wargames, or simulations for senior decision-makers and published on the subject
Have training in a discipline that uses simulation, experimental methods or structured analysis
Have 10+ years of experience in national security, government, technology policy, or strategy
Can keep the scenarios technically grounded with a strong grasp of AI capabilities and safety research
Have managed a team, including hiring, setting direction, giving feedback and developing people
Use Claude and other AI tools extensively in your own work
Care about AI safety and Anthropic's public benefit mission
Preferred qualifications
Published research on wargaming methods, simulation or crisis decision-making
Run exercises that fed into real policy or crisis-response decisions
Run multi-iteration or experimental wargames and analyzed results across runs
Used AI agents or language models as players, adjudicators or analysts in simulations
Led a research program or team through a period of growth
Existing relationships across national security, government, policy and AI, and are trusted in those communities
Published widely read work on AI, emerging technology, strategy or crisis decision-making
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.
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$295,000—$295,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
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.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
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 gr