Anthropic
Research Engineer, Economic Research Data Platform
About this role
Anthropic seeks a Research Engineer to build and maintain data infrastructure supporting economic research on AI's impact. You'll design scalable pipelines, create ML systems for analyzing Claude usage patterns, and develop tools enabling researchers to access privacy-preserving datasets.
What you'll do
- Build and operate data pipelines that process raw usage data into clean, privacy-preserving datasets
- Design systems including classifiers and ML pipelines to measure Claude's economic impact
- Create self-serve workflows to ingest and integrate external data sources
- Develop APIs, libraries, and interfaces for serving data to researchers and the public
- Collaborate with researchers, data scientists, and policy experts across the organization
- Ensure data reliability, integrity, and privacy compliance across economic research infrastructure
What they're looking for
- Data pipeline development and optimization
- Python programming with clean, documented code
- Cloud infrastructure (AWS or GCP)
- Machine learning systems and classifier development
- Analytics workflows and data transformation frameworks
- Privacy-preserving data systems
- API and web service development
- Cross-functional communication with non-technical stakeholders
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through a complex data pipeline you've built in production. What were the key design decisions, and how did you handle data quality and reliability?
- Describe your experience with cloud infrastructure (AWS/GCP). How have you balanced engineering standards with moving quickly in ambiguous situations?