Staff Engineer, Datacenter Server Lifecycle

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.

Anthropic is expanding beyond cloud infrastructure, and this role sits at the heart of that effort. As a Staff Engineer on the Datacenter Server Lifecycle team, you will own the end-to-end operational journey of every machine in our facility — from initial provisioning and deployment, across its working life, through maintenance and refresh, and all the way to decommissioning. This is greenfield work: you will help define the processes, tooling, and operational standards that govern how we run and retire hardware at scale.

A distinguishing aspect of this role is its deep intersection with security. The machines in our datacenter handle some of the most sensitive workloads in AI — training frontier models and serving millions of users interacting with Claude. Ensuring that every machine in the fleet is trusted, attested, and operating with a verified chain of integrity from the hardware up is a core part of the job, not an afterthought. You will partner closely with our Infrastructure Security team to define and enforce trusted compute standards across the lifecycle, from secure provisioning through end-of-life handling.

Key responsibilities

Minimum qualifications

• Lead the build-out of automation to support datacenters containing tens of thousands of servers.
• Define and own the end-to-end server lifecycle strategy — from provisioning and deployment through operation, maintenance, refresh, and decommissioning — and maintain automation and operational procedures for common lifecycle events (e.g., hardware failures, firmware upgrades, fleet rotations).
• Partner closely with Infrastructure Security to design and enforce trusted compute standards across the server lifecycle.
• Work closely with our Networking team to ensure end-to-end connectivity across all sites.
• Build and maintain tooling to track machine health, configuration, and operational status across the full datacenter fleet.
• Hands-on experience with server hardware, including rack deployment, cabling, troubleshooting, and understanding failure modes at scale.
• End-to-end understanding of hardware lifecycle management: asset tracking, provisioning workflows, maintenance scheduling, and decommissioning practices.
• Proficiency in at least one programming language (e.g., Python, Rust, Go, or Java).
• Working knowledge of modern cloud infrastructure, including Kubernetes, Infrastructure as Code, AWS, and GCP.
• Ability to communicate clearly and build consensus with a wide range of stakeholders.
• Comfort navigating ambiguity and making progress on complex, cross-functional problems.
• Willingness to travel occasionally to datacenter sites across North America.

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.

• 8+ years of experience in datacenter operations, hardware infrastructure management, or a closely related discipline.
• Hands-on experience with GPU or AI accelerator hardware (e.g., NVIDIA A100/H100, AMD MI300, Google TPUs, or AWS Trainium) and an understanding of their operational demands.
• Familiarity with modern provisioning tooling such as coreboot, LinuxBoot, or u-root.
• Experience building or contributing to datacenter automation or fleet management platforms.

$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.