Data Engineer (AI Safeguards)

TL;DR

Data Engineer (AI Safeguards): Designing and building the data foundations and pipelines that power AI safety monitoring, abuse detection, and trust efforts with an accent on scalable warehousing and analytical tooling. Focus on building robust ETL/ELT pipelines, optimizing data models for large-scale safety data, and implementing reliability frameworks for safety-critical systems.

Location: Hybrid (San Francisco, CA or New York City, NY). Must be in office at least 25% of the time.

Company

Anthropic is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems that are safe and beneficial for society.

What you will do

• Design and maintain scalable data pipelines for safety monitoring, abuse detection, and enforcement workflows.
• Develop and optimize data models and warehousing solutions for large-scale usage and safety data analysis.
• Build dashboards and reporting infrastructure to provide visibility into model behavior and misuse patterns.
• Integrate data from multiple sources, including model outputs, user reports, and automated classifiers.
• Implement data quality frameworks, monitoring, and alerting for safety-critical data.
• Collaborate with research teams to surface data insights that inform model improvements.

Requirements

• Proficiency in SQL and Python with hands-on experience building ETL/ELT pipelines.
• Experience with cloud data platforms such as BigQuery, Redshift, or Snowflake.
• Experience with modern data stack tools like dbt, Airflow, or Spark.
• Ability to build dashboards using Looker, Tableau, or Metabase.
• Must be based in or be able to work from San Francisco or New York City (Hybrid policy: 25% office presence).
• Bachelor’s degree or equivalent professional experience in a relevant field.

Nice to have

• 8+ years of experience in data engineering or analytics engineering.
• Background in trust and safety, integrity, fraud, or abuse detection systems.
• Experience with event streaming systems like Kafka, Pub/Sub, or Kinesis.
• Experience building data infrastructure for ML model monitoring or evaluation.
• Familiarity with data privacy and compliance frameworks such as GDPR or CCPA.

Culture & Benefits

• Competitive compensation and optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours and modern office spaces.
• Visa sponsorship availability for qualified candidates.
• Highly collaborative environment focused on large-scale, high-impact AI research.