Research Engineer, Economic Research Data Platform

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. The Economic Research team is part of the Anthropic Institute, and studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data, including the Anthropic Economic Index, for the benefit of the public – helping policymakers, businesses, and workers understand and navigate the transition to powerful AI. The questions we work on include: how is AI changing jobs and economic activity, who is adopting it and why, and what determines whether a region or industry captures value from it. In this role, you will work closely with teams across Anthropic — includ [... source excerpt omitted ...] rack record building data processing pipelines, architecting and implementing high-quality internal infrastructure, working in a fast-paced environment, and navigating ambiguity. Responsibilities: 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 [... source excerpt omitted ...] oss all economic research data infrastructure You might be a good fit if you: Have significant experience building data-intensive applications, pipelines, or internal tooling in production Have experience with cloud infrastructure platforms such as AWS or GCP, and take pride in writing clean, well-documented code in Python that others can build upon Have intuition for analytics workflows and empathy for how researchers and data scientists work Are comfortable making technical decisions with incomplete information while keeping engineering standards high Have a "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end, even if it requires going out