Research Engineer, Economic Research Data Platform
San Francisco, CA
Posted 17d ago
About the job
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 studies the economic implications of AI on individual, firm, and economy-wide outcomes, building scalable systems to monitor AI usage patterns and measure the impact of AI adoption on real-world outcomes. You will collaborate closely with teams across Anthropic, including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy, to build scalable and robust data systems supporting high-leverage, high-impact research.
Responsibilities
- Build and operate data pipelines to transform raw usage data into clean, privacy-preserving datasets.
- Design new systems, including classifiers and ML pipelines, to understand Claude's usage and economic impact.
- Build self-serve workflows for ingesting and integrating external data sources.
- Develop APIs, libraries, and interfaces for researchers and the public to access data.
- Partner with researchers, data scientists, policy experts, and cross-functional partners to advance Anthropic's safety mission.
- Contribute to team roadmap, documentation, and practices for self-serve data access while maintaining safety and governance.
- Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure.
Requirements
- Significant experience building data-intensive applications, pipelines, or internal tooling in production.
- Experience with cloud infrastructure platforms like AWS or GCP.
- Proficiency in writing clean, well-documented Python code.
- Intuition for analytics workflows and empathy for researchers and data scientists.
- Comfort making technical decisions with incomplete information while maintaining high engineering standards.
- A 'full-stack mindset' to solve problems end-to-end.
- Strong communication skills for effective collaboration with diverse technical and non-technical partners.
- A commitment to the societal impacts of work and interest in AI's economic implications.