AI Infrastructure Operations, Demand Planning

AI Chopping Block; New York (NY); Hybrid; USD 320,000 - 405,000; Full time

Job summary

Anthropic is seeking a Capacity Planner to translate Demand Planning forecasts into per-tranche requirements and to manage the end-to-end schedule from contracted to occupied capacity. You will partner with research, pretraining, inference, and compute teams to ensure capacity plans are executable and measurable.

You will own the integrated schedule, drive readiness automation, and produce executive reports on variance, risk, and delivery dates across multiple regions and data centers.

Qualifications

• Significant experience delivering large-scale infrastructure — cloud regions, accelerator clusters, HPC systems, or bare-metal fleets — at multi-region scale or ≥10k accelerators (or CPU/storage equivalent).
• Technical range from cluster orchestration and node health to telemetry and planning tables to debug divergences rather than route them.
• SQL and enough Python to answer your own questions and build your own reporting.
• A degree in a technical field or an equivalent engineering track record.

Responsibilities

• Turn the forecast into per-tranche requirements for accelerator, interconnect, region, and date.
• Qualify tranches for deliverability before signature.
• Close the delivery loop by tracking forecast-versus-delivered and feeding variance back into planning.
• Own the bring-up system of record and define the contract-to-occupied state machine.
• Run a portfolio of bring-ups across new cloud regions, on-prem sites, and neocloud blocks with an integrated schedule.
• Drive readiness automation across capacity systems from contracted through ingested and scaled.
• Instrument and publish the numbers that matter with executive-level reporting.

Skills

Large-scale infrastructure experience

SQL

Python

Technical degree

Education

Bachelor's degree in a technical field

Job description

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

Anthropic runs one of the largest and fastest-growing infrastructure fleets in the industry, across multiple accelerator families, CPU families, clouds, neoclouds, and on-prem sites. Capacity Engineering owns the data, tooling, and systems that let Anthropic plan, measure, and maximize utilization of that fleet: we partner on supply deals, wire telemetry from day zero, own the canonical capacity data layer, and build the planning and enforcement tools every research and product team relies on. This role sits in the Planning pillar, on the Demand Planning team, and works daily with research engineering, pretraining, inference, compute supply, finance, and external vendors.

You own the tranches. The job has two halves that feed each other. Upstream, you take the Demand Planning forecast and turn it into per-tranche requirements — shape, interconnect, region, supporting resources, date — and carry those into sourcing negotiations and data center build reviews so we contract for capacity we can actually use when we need it. Downstream, you own the integrated schedule and system of record for every tranche in flight — from contracted through reserved, ingested, in-cluster, healthy, and occupied — and you drive the owners of each hop to their dates. Every slip you see downstream becomes a contract-language fix, an automation, or a correction fed back to the forecast.

What you’ll do

• Turn the forecast into per-tranche requirements. Take the Demand Planning forecast plus direct input from research, pretraining, and inference planners, and convert it into concrete accelerator, interconnect, region, supporting-resource, and date requirements for each tranche. Represent those in sourcing negotiations and data center build reviews, including which contractual terms actually move delivery dates.
• Qualify tranches for deliverability before signature. The Capacity Planner signs fit-to-forecast; you sign whether the shape can land schedulable, healthy, and instrumented in that region on that date, with storage, egress, identity in place.
• Close the delivery loop. Track forecast-versus-delivered on shape, region, and timing for every tranche; publish the variance; and feed it back to Demand Planning and into the next contract.
• Own the bring-up system of record. Define the canonical contract-to-occupied state machine with explicit entry and exit criteria per stage, and make it a first-class object in the capacity data layer so every downstream tool sees in-flight capacity, not only what has landed.
• Run a portfolio of bring-ups in parallel — new cloud regions, on-prem sites, neocloud blocks — with one integrated schedule spanning provider milestones, cluster creation, network turn-up, storage readiness, health burn-in, and first-workload landing.
• Drive readiness automation: All capacity systems are fully integrated for all new capacity, from contracted through ingested, automated and scaled.
• Instrument and publish the numbers that matter — time-to-occupied and paid-idle dollars per tranche — with executive-level reporting on status, tradeoffs, and risk across the portfolio.

What you bring

• Significant experience delivering large-scale infrastructure — cloud regions, accelerator clusters, HPC systems, or bare-metal fleets — at multi-region scale or ≥10k accelerators (or CPU/storage equivalent).
• Technical range from through cluster orchestration and node health, up to the telemetry and planning tables on top — enough to debug where they disagree rather than route it.
• SQL and enough Python to answer your own questions and build your own reporting.
• A degree in a technical field or an equivalent engineering track record.

Preferred

• Reserved-capacity onboarding, private offers, or capacity commitments with cloud or neocloud providers.
• Enough demand-planning exposure to challenge a forecast, translate it into per-tranche requirements, and feed delivery variance back into it.
• Data center or colocation delivery: power and space planning, network turn-up, site acceptance, vendor management.
• Accelerator health and burn-in, collective-communications sanity testing, or fleet-health SLOs — and a rigorous definition of "healthy."
• Systems of record or lifecycle services for infrastructure assets.
• Onboarding a new hardware generation into an existing scheduler and observability stack.

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 think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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.