Staff+ Software Engineer, Safeguards Data at Anthropic

Hybrid - San Francisco, CA; New York City, NY

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Anthropic seeks a Staff+ Software Engineer for the Safeguards team to build and operate data platforms that power safety and oversight mechanisms for AI systems. Responsibilities include designing ingestion and processing pipelines, maintaining data governance and integrity, ensuring portability across cloud providers, and collaborating with analysts, investigators, and researchers to support detection and review workflows. The role is hybrid, requiring onsite presence in San Francisco or New York City at least 25% of the time.

Salary

USD 320,000 - 485,000

Requirements

Skills

  • Proficiency in Python and SQL
  • Experience building and operating data pipelines or data stores in production
  • Ability to work across the data stack, including ingestion, storage, and consumption
  • Strong written and verbal communication skills
  • Extensive experience as a software engineer, including significant time on data‑intensive systems
  • Experience with integrity, spam, fraud, or abuse detection and mitigation
  • Experience building trust and safety detection and intervention mechanisms for AI or machine learning systems
  • Experience building and operating large‑scale distributed data infrastructure
  • Experience working across multiple cloud providers, or building infrastructure designed to be provider‑agnostic
  • Experience meeting data governance requirements in a regulated or high‑sensitivity domain
  • Experience working closely with operational teams to build custom internal tooling

Responsibilities

  • Build and operate the data platform that powers Safeguards, including ingestion and processing pipelines, warehouses and other data stores, and the schemas and interfaces that detection and review systems depend on
  • Keep Safeguards systems running day to day and hold a high operational bar that serves both safety and customers, while reducing the manual effort needed to sustain it
  • Own data governance and integrity, including retention and access controls, privacy‑preserving handling of sensitive data, lineage, and correctness guarantees that downstream consumers can rely on
  • Design systems that run portably across cloud providers, working within the constraints of customer‑managed and third‑party environments
  • Partner with the analysts, investigators, and researchers who rely on this data, and build the internal tooling their work depends on

Technologies

PythonSQLAWSGCPAzuredata pipelinesdata stores

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