Research Engineer, Economic Research Data Platform

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 role As a Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis.

What you’ll do

• As a Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis.
• Build and operate the data pipelines that turn raw usage data into clean, reusable, privacy-preserving datasets
• Design new systems - including developing classifiers, training probes on model internals, and building the ML pipelines behind them — for understanding how Claude is used and the impact it's having on the economy
• Build self-serve workflows to ingest and integrate external data sources so they're interoperable with internal datasets
• Develop the APIs, libraries, and interfaces that serve data to researchers and the public
• Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic's safety mission
• Contribute to the team roadmap, documentation, and practices that enable self-serve data access while maintaining safety and governance standards
• Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure

What they’re looking for

Select a requirement to inspect fit, evidence or application context.

• Experience with modern data transformation, orchestration, and query frameworks
• Building systems and products on top of LLMs
• Privacy-preserving data systems, or data governance and lineage tooling
• Building and operating web services and the infrastructure underneath them
• Full-stack development or complex data visualization
• Background in econometrics, statistics, or quantitative social science
• Working in environments where engineers partner closely with quantitative users — research labs, trading firms, analytics companies
• A field relevant to the role as demonstrated through coursework, training, or professional experience