Anthropic
[C] Data Engineer, Safeguards
About this role
Anthropic seeks a Data Engineer to build foundational data systems for the Safeguards team, enabling AI safety monitoring, abuse detection, and enforcement at scale. You'll design pipelines, warehousing solutions, and analytical tooling that help teams detect misuse patterns, measure safety interventions, and make data-driven decisions about model behavior.
What you'll do
- Design and maintain scalable ETL/ELT pipelines for safety monitoring, abuse detection, and enforcement workflows
- Develop optimized data models and warehouse solutions for analyzing large-scale usage and safety data
- Build dashboards and reporting infrastructure providing visibility into model behavior and misuse patterns
- Integrate data from multiple sources including model outputs, user reports, and automated classifiers into unified analytical layer
- Implement data quality frameworks, monitoring, and alerting for safety-critical data
- Develop self-service data tools enabling stakeholders to independently explore and report on safety data
What they're looking for
- SQL and Python
- Cloud data platforms (BigQuery, Redshift, Snowflake)
- Data orchestration and transformation (dbt, Airflow, Spark)
- Data visualization and dashboarding (Looker, Tableau, Metabase)
- ETL/ELT pipeline design and maintenance
- Data governance and privacy compliance
- Event streaming systems (Kafka, Pub/Sub, Kinesis)
- ML model monitoring and evaluation infrastructure
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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 pipeline you've built—what were the scaling challenges and how did you address them?
- How have you approached data quality and reliability in safety-critical or high-stakes systems?