AI Infrastructure Operations, Demand Planning

Anthropic · San Francisco, CA | New York City, NYmid

In short

▸Planificación de demanda de infraestructura AI a gran escala en múltiples regiones y proveedores.
▸Gestión diaria de tranches de capacidad desde contrato hasta ocupación activa, con enfoque en fechas, regiones y recursos.
▸Destacado: Eres el dueño del sistema de registro único de estado de cada tranche, con automatización y reportes ejecutivos.

Proficiency in English required for collaboration across global teams.

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What they ask for

✓Experiencia comprobada en despliegue de infraestructura a gran escala (≥10k aceleradores o equivalente en CPU/almacenamiento).
✓Capacidad para convertir pronósticos en requisitos concretos de forma técnica y operativa.
✓Experiencia trabajando con múltiples regiones, proveedores cloud, on-prem o neocloud.
✓Habilidad para gestionar múltiples proyectos en paralelo con fechas críticas.
✓Conocimiento en sistemas de telemetría, data layer y herramientas de planificación de capacidad.
✓Capacidad de análisis y retroalimentación de variaciones entre pronóstico y entrega real.

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Capacity PlanningSourcing NegotiationsData Center Build ReviewsContractual TermsAccelerator ClustersHPC SystemsCloud RegionsOn-Prem SitesNeocloud BlocksIntegrated Schedule

Who should you write to at Anthropic?

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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).</

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