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Salary: USD 320,000 - 485,000 / annual
Anthropic is seeking a Principal Engineer to lead the Capacity Engineering team, which manages 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 that power capacity management: data pipelines ingesting and normalizing telemetry from heterogeneous cloud environments, observability tooling providing real-time fleet health visibility, and performance instrumentation measuring workload efficiency. 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 and overlapping problems):
**Data Platform**: Pipelines ingesting occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage across cloud providers, and serving BigQuery tables company-wide. Focus on correctness, completeness, and latency. Consumers range from research engineers to finance to leadership, requiring product thinking: schema contracts, discoverability, and understanding actual consumer needs.
**Planning**: Making fleet state legible and actionable in real time. Cluster health tooling, capacity planning platforms, alerting on occupancy issues and allocation problems, and systemic fixes to scheduling and fragmentation. Balances Kubernetes operations with cross-team coordination.
**Efficiency**: Measuring and improving how effectively every major workload uses its hardware. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close gaps. Metrics must be defensible to the teams responsible for them.
**Attribution and Forecasting**: Connecting fleet costs to business activities. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to workloads and teams, and turning inference demand signals and research roadmaps into defensible compute plans. Efficiency metrics must survive finance scrutiny: stripped of pure 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, job-level utilization), owning attribution and forecasting, building the data platform with real ownership of completeness and latency SLOs, operating Kubernetes-native systems at scale, and treating outputs as products with schema contracts and consumer-focused design.
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, on-demand capacity reservations, commitments, and vendor telemetry
- 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
