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Anthropic Fellows Program, The Anthropic Institute (Economics & Policy)

Anthropic· AI Research & Engineering
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Hybrid (London | Ontario | United States | San Francisco)$3.9kfellowshipPosted 6mo ago

About the role

Fellows program to fund and mentor promising technical talent to conduct AI research/engineering with public outputs, ongoing cohorts beginning January 2027; remote-friendly with regional work authorization required.

Responsibilities

  • Designing and conducting empirical research on AI's economic effects, drawing on external data sources
  • Developing new methodological approaches for studying AI's impact on labor markets, the future of work, and society
  • Analysing the offense–defense balance for AI-enabled cyber and bio capabilities as models scale
  • Measuring the extent to which model performance increases with custom harnesses
  • Identifying market driven mechanisms that could improve societal resilience to anticipated threats from AI systems
  • Identifying which metrics relating to AI R&D could serve as early warning signals for recursive self-improvement

Qualifications

  • Fluent in Python programming
  • Available to work full-time on the Fellows program
  • Interest in economics or policy research (prior experience a plus but not required)
  • Strong track record of empirical research
  • Thrives in fast-paced, collaborative environments
  • Ability to implement ideas quickly and communicate clearly

Skills

Required:PythonEmpirical researchWriting and communication

Benefits

  • Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (vary by country)
  • Substantial funding for compute and other research expenses
  • Mentorship from Anthropic researchers
  • Opportunity to produce public outputs (e.g., papers)
  • Remote-friendly with potential regional workspace options
Last updated Oct 7, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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