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Salary: USD 320,000 - 485,000 / annual
Anthropic is seeking a Principal Engineer to lead the Capacity Engineering team, responsible for managing one of the largest and fastest-growing infrastructure fleets in the industry. This role owns the data, tooling, and operational systems that enable Anthropic to plan, measure, and maximize utilization across first-party and third-party compute resources spanning multiple accelerator families, CPU families, and cloud providers.
You will build production systems powering this work: data pipelines ingesting and normalizing telemetry from heterogeneous cloud environments, observability tooling providing real-time fleet health visibility, and performance instrumentation measuring workload efficiency across hardware. You'll write production-quality code daily, operate Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic's largest spending areas.
The role spans four interconnected areas with fluid boundaries:
**Data Platform**: Build pipelines ingesting occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve BigQuery tables company-wide. Focus on correctness, completeness, and latency. Treat this as product work: define schema contracts, ensure data discoverability, and understand what consumers (research engineers, finance, leadership) actually need.
**Planning**: Make fleet state legible and actionable in real time. Build cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Balance Kubernetes operations with cross-team coordination.
**Efficiency**: Measure and improve how effectively every major workload uses its hardware. Instrument utilization across training, inference, and eval systems, build benchmarking infrastructure, establish per-config baselines, and work directly with system-owning teams to close gaps. Metrics must be defensible to the teams responsible for them.
**Attribution and Forecasting**: Connect fleet costs to business activities. Reconcile CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attribute spend to workloads and teams, and turn inference demand signals and research roadmaps into defensible compute plans. Efficiency metrics must survive finance scrutiny: stripped of demand and unit-price effects, reproducible month-over-month, and legible to a CFO.
Key responsibilities include building the planning and allocation stack, driving efficiency programs (stranding, rightsizing, unused capacity recovery), owning attribution and forecasting, building the data platform with real ownership of completeness and SLOs, operating Kubernetes-native systems at scale, and treating outputs as products with schema contracts and on-call support.
You'll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires comfort moving between data engineering, systems engineering, and observability, and thriving in high-autonomy, high-ambiguity environments.
**Requirements:**
- Strong track record building and operating production systems (hands-on engineering with DevOps flavor)
- Production-quality Python and SQL (idiomatic, well-tested, maintainable code)
- Deep experience with at least one major cloud provider (AWS, GCP, or Azure) and its operations
- Experience with observability tooling stack (Prometheus, PromQL, Grafana), including writing recording rules and building monitoring that engineering teams rely on
- Ability to gather own requirements and work across organizational boundaries in ambiguous environments with limited direction
- Bachelor's degree or equivalent combination of education, training, and professional experience in a field relevant to the role
**Preferred Qualifications:**
- Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or large-scale ML environment
- Scheduling and packing efficiency experience or profiling-driven optimization of large distributed workloads
- Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, 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 via DCGM, TPU utilization, Trainium power/utilization, or ML training/inference systems at hardware level)
- Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability
- Storage efficiency, retention, and lifecycle program experience at exabyte scale
