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Anthropic Pbc D B A Anthropic INC

Tabletop Exercise Lead, The Anthropic Institute

San Francisco, CA Posted today

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordUSCIS Data Hub records 1 H-1B approvals for this employer in FY2023 — older 2023 data; no recent Department of Labor filings (Oct 2025 – Jun 2026) were found for it.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
USCIS Data Hub records 1 H-1B approvals for this employer in FY2023 — older 2023 data; no recent Department of Labor filings (Oct 2025 – Jun 2026) were found for it. Source: US Department of Labor visa filings (LCA) (US Department of Labor (OFLC), plus USCIS H-1B Employer Data Hub).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

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About the role

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

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