Anthropic
Data Engineer, Safeguards
About this role
Anthropic is seeking a Data Engineer for its Safeguards team to develop robust data infrastructure that enhances AI safety measures. This role involves building data pipelines, optimizing data models, and collaborative efforts to monitor misuse and improve AI interventions.
What you'll do
- Design and maintain scalable data pipelines for safety monitoring
- Develop data models and warehousing solutions for analysis
- Build dashboards for visibility into model behavior and misuse
- Integrate data from various sources into a unified layer
- Implement data quality frameworks and monitoring
- Support research teams with data insights for model improvements
What they're looking for
- Proficiency in SQL and Python
- Experience with cloud data platforms like BigQuery or Snowflake
- Familiarity with ETL/ELT pipelines and orchestration tools
- Ability to create data visualizations using Looker or Tableau
- Strong communication skills for technical concepts
- Understanding of data privacy frameworks
Benefits
- Competitive salary range from $320,000 to $405,000
- Opportunity to work on impactful AI safety initiatives
- Collaborative environment with experts across multiple fields
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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 ETL/ELT pipeline you've built. What data sources did you integrate, what transformations did you apply, and how did you handle data quality issues?
- Describe your experience with cloud data platforms like BigQuery, Redshift, or Snowflake. How have you optimized queries or data models for cost and performance at scale?