The opportunity
Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated.
What you'll do
Data platform Pipelines that ingest occupancy and utilization telemetry from: Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need.
Planning Knowing what the fleet has, where it's going, and what's in the way.: Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other.
Efficiency Measuring and improving how effectively every major workload uses: the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number.
Attribution and forecasting Connecting what the fleet costs to what the: business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO.
Build the planning and allocation stack: the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs.
Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale.
What they're looking for
- Experience with capacity planning, resource management, or cost attribution: systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here.
- Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads.
- Multi-cloud data ingestion experience, especially normalizing billing: exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements.
- Total cost of ownership and forecasting experience, including decomposing: whether infrastructure growth is causal or correlated with business drivers.
- Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization,: Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level.
- Experience building internal data products with self-service access, schema: contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed.
- Storage efficiency, retention, and lifecycle program experience at exabyte scale.
