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Salary: USD 320,000 - 485,000 / annual
Anthropic is seeking a Staff Software Engineer to build and operate the data infrastructure powering its Safeguards team, which focuses on monitoring AI models, preventing misuse, and supporting user well-being. This role centers on designing and maintaining the data foundations—pipelines, stores, and governance controls—that detection, evaluation, and review systems depend on, deployed across AWS, GCP, and Azure.
Key responsibilities include building and operating the data platform that powers Safeguards, including ingestion and processing pipelines, warehouses, and the schemas and interfaces that downstream systems rely on. You will keep Safeguards systems running reliably day-to-day while maintaining a high operational bar that serves both safety and customers, reducing manual effort needed to sustain operations. Data governance and integrity are core to this role: you will own retention policies, access controls, privacy-preserving handling of sensitive data, lineage tracking, and correctness guarantees that downstream consumers depend on.
You will design systems that run portably across cloud providers, working within the constraints of customer-managed and third-party environments. You will partner closely with analysts, investigators, and researchers who rely on this data, building the internal tooling their work depends on. The systems you build sit underneath decisions with real consequences for customers and people affected by model misuse; correctness, retention discipline, and access control are core engineering requirements rather than afterthoughts.
Minimum qualifications include proficiency in Python and SQL, production experience building and operating data pipelines or data stores, ability to work across the full data stack (ingestion, storage, consumption), and strong written and verbal communication skills. Preferred qualifications include extensive software engineering experience on data-intensive systems, experience with integrity/spam/fraud/abuse detection, experience building trust and safety mechanisms for AI systems, large-scale distributed data infrastructure experience, multi-cloud or provider-agnostic infrastructure experience, data governance experience in regulated or high-sensitivity domains, and experience building custom internal tooling with operational teams.
