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
Anthropic is rapidly scaling our compute infrastructure across a portfolio of datacenter builds with multiple developer, neocloud, and cloud partnerships. We're looking for a DC Portfolio Planning & Execution Lead to drive the planning and framework that ensures every site moves smoothly from the front-end phases through design, construction, equipment delivery, commissioning, and operational readiness.
This role owns the portfolio-level operating system: translating capacity supply pipeline into integrated project plans that span every phase of delivery, building the tooling and automation that runs it at scale, and maintaining Anthropic's datacenter capacity catalog — a lifecycle view of our fleet that supports both execution orchestration and steady-state capacity planning. You will build this function from the ground up.
Responsibilities
Portfolio schedule & catalog
Manage the integrated master plan for each site across the portfolio — stitching power ramp, design, construction, sourcing, deployment, and operations readiness into a single coordinated schedule with clear milestones and dependencies
Develop and maintain Anthropic's datacenter catalog for deployed and in-progress capacity. Manage the portfolio-level view of physical infrastructure & cluster interfaces across all sites and partners to enable planning decisions such as equipment fungibility, accelerator platforms, tech insertion, or workload allocation
Stage gates & execution tracking
Define and run the stage gates and decision locks for cluster delivery — from lease execution to design lock through procurement, construction, equipment installation, commissioning, and handover
Drive gate reviews, manage exceptions, and track the downstream impact of deviations across the portfolio
Manage portfolio reviews and risk tracking for DC Infra leadership and Compute Supply
Tooling & process
Develop tooling and automation to enable cross-functional planning flow-down from datacenter capacity availability dates
Partner with Design, Supply Chain, Construction, and DC Ops program leads to drive cross-pillar process improvements as portfolio scales
You may be a good fit if you
Are familiar with the full datacenter buildout lifecycle: pipeline → design → sourcing → construction → Cx → deployment
Have run integrated portfolio or master-schedule planning across a fleet of capital projects (datacenter, energy, fab, or similar) where multiple functional orgs each own a phase
Have built a stage-gate or decision-lock system from scratch and gotten functional leads to adopt it
Have re-architected a deployment or delivery process at scale and can point to the cycle-time or throughput result
Build the tooling yourself using AI-assisted development — stand up planning dashboards, schedule automation, and data pipelines from Smartsheet/P6/partner systems
Proactively surface schedule risk across functions — comfortable flagging a problem in someone else's domain before it becomes a slip
Track record of driving outcomes through influence with cross-functional partners
Strong candidates may also have
Experience building a portfolio planning and execution function from scratch at a hyperscaler or large industrial owner
Exposure to capacity planning or S&OP processes that connect demand forecast to physical build
Experience product-managing internal planning, workflow, or scheduling systems
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.
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.
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.