Security Data Engineer
Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Oct 26, 2026. Anthropic scores B on the Alion truth index.
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 built metrics a team made decisions on and defended them when challenged
Have worked hands-on with data about physical assets or security controls: a hardware fleet, data center or supply chain inventory, asset custody, or the control and evidence data of a security or compliance program
Read and review other people's code, and build with AI coding agents using Git and code review
Own a domain end to end: set the approach, design and build it with the teams and vendors whose systems it depends on, and run it as something people use every day
Write SQL and at least one general-purpose programming language well
Preferred qualifications
Experience with asset management, supply chain or warehouse systems where serialized tracking and custody transfer are the core problem
Experience building continuous control monitoring or metrics for a security or compliance program, or producing evidence an assessor accepted, for example against NIST SP 800-53, ISO 27001 or SOC 2
Experience with risk or posture scoring that combines control results, findings and other evidence into a measure people make decisions on
Familiarity with data center hardware and its lifecycle from receipt to decommission, and with sites and parties you did not control: colocation, cloud or GPU cloud operators, hardware manufacturers, logistics providers
Have run software on many hosts or at remote sites with versioned rollout and a way to know each copy is still reporting
Have built AI agents or automation that replaced recurring manual work
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$320,000—$405,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links-visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.