Security Data Engineer

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role Claude is trained and served on hardware in data centers Anthropic operates and in sites partners build and run for us. DCSE secures that footprint: the sites, the hardware moving through them, and the people and vendors who touch it. Security at that scale runs on data, and you'll be the engineer who owns it and the systems that produce it: component-level hardware inventory and chain of custody, fed by software that runs at our sites and on hosts, and continuous control monitoring, with the posture calculations that decide what each site is cleared to run.

You set the architecture, agree schemas and interfaces with the teams, operators and vendors that own the sources, build the pipelines, stores and tools, and run what you ship. The goal is security that runs itself: custody, access and evidence verified continuously from records, with tools and AI agents doing the reviews. We favor simple, standard controls and tools that can be verified over elaborate ones. We use Claude throughout our engineering workflow and expect you to as well.

You'll work with the rest of DCSE, including the engineers on its security monitoring platform, with Anthropic's compute and data center operations teams, and directly with data center operators, hardware vendors and logistics providers. The role is based in San Francisco, with remote applicants in the US considered, and involves regular travel to data center sites, which are critical infrastructure.

Key responsibilities

Hardware inventory and chain of custody: own the component-level record of what hardware exists, where it is and who has handled it: the data model, the pipelines that join inventory and custody events from many source systems, change history on every record, and the tamper-evident stores behind it

Tools, automation and AI agents: build the tools and agents the team runs on this data: reconciliation, audit evidence exports, custody checks. When a review keeps costing people time, you build the tool or agent that retires it

Custody workflows and field software: build the workflows and the site and host software that capture custody events as they happen (a receipt, a move, a data wipe, a decommission) and enforce who is authorized to record them

Continuous control monitoring and posture: own the pipelines that turn check outcomes, assessment results and findings into per-control and per-site measures over time, against the compliance frameworks we and our partners are assessed against, and the posture calculation that decides what a site is cleared to run

Data quality: build coverage checks, reconciliation against sources and alerts on a silent or drifting feed, so a wrong or missing record is caught the same day; own retention and access controls on the records

Integration and reporting: agree schemas and interfaces with the engineers who run the security monitoring platform, and build the query surfaces and reports the team, site leads and leadership use directly

Minimum qualifications

Have designed and run production data systems (relational schemas, change data capture, batch or streaming pipelines and the services around them) and kept them correct: coverage measured, records reconciled against the source, alerts on silence, and a design that expects records to get corrupted or lost

Have integrated with systems you did not control (inventory tools, ticketing systems, vendor APIs) and built data models that outlive any one source

Have shipped software that runs outside a central service (host agents, site-local services, edge collectors) and kept it correct in the field, or...