Research Engineer, Economic Research

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The Economic Research team studies the economic implications of AI on individual, firm, and economy-wide outcomes and builds scalable systems to monitor AI usage patterns and measure the impact of AI adoption. The team publishes research and data to help policymakers, businesses, and the public understand and navigate the transition to powerful AI.

This role designs, builds, and maintains critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems across Anthropic, including research tools for privacy-preserving analysis (e.g., CLIO), and collaborate closely with Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy.

Responsibilities

• Build and maintain data pipelines that process large-scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.
• Expand privacy-preserving tools to enable new analytic functionality to support research needs.
• Design and implement novel data systems leveraging language models (e.g., CLIO) where traditional software engineering patterns don't yet exist.
• Develop and maintain interoperable data pipelines across data sources (including ingesting external data) designed to support economic analysis.
• Contribute to the strategic development of the economic research data foundations roadmap.
• Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure.
• Lead technical design discussions to ensure infrastructure supports current needs and future research directions.
• Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards.
• Partner with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission.

Requirements

• Experience working with research scientists and economists on ambiguous AI and economic projects.
• Experience building and maintaining data infrastructure, large datasets, and internal tools in production environments.
• Experience with cloud infrastructure platforms such as AWS or GCP.
• Strong Python skills; pride in writing clean, well-documented code.
• Comfortable making technical decisions with incomplete information while maintaining high engineering standards.
• Ability to get up to speed quickly on unfamiliar codebases and collaborate across diverse engineering backgrounds.
• Track record of using technical infrastructure to interface effectively with machine learning models.
• Experience deriving insights from imperfect data streams.
• Experience building systems and products on top of LLMs and incubating/maturing tooling platforms used by many stakeholders.
• Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners.

Strong candidates may have

• Background in econometrics, statistics, or quantitative social science research.
• Experience building data infrastructure and data foundations specifically for research.
• Familiarity with large language models, AI systems, or ML research workflows.
• Prior work on projects related to labor economics, technology adoption, or economic measurement.

Logistics

• Location: San Francisco, CA. Anthropic is headquartered in San Francisco and expects staff to be in one of its offices at least 25% of the time (location-based hybrid policy).
• Education: At least a Bachelor's degree in a related field or equivalent experience.
• Visa sponsorship: Anthropic states they do sponsor visas and will make reasonable efforts to obtain a visa for candidates they hire, retaining an immigration lawyer to help.

Compensation

• Annual Salary: $300,000 - $405,000 USD

Benefits

Anthropic states they offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a collaborative office environment.