320K–485K USD / year
Principal Engineer, Capacity/Compute
DevOpsHybrid — San Francisco, CA
Published on 2026-10-18
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About this role
Build and operate systems that help Anthropic track, plan, allocate, and optimize its large, multi-cloud compute fleet. The role spans data pipelines, Kubernetes operations, observability, capacity planning, utilization efficiency, and cost attribution.
About the company
Anthropic builds reliable and interpretable AI systems, including the Claude family of models, for businesses and developers. The company focuses on AI safety research and practical AI assistants for enterprise and consumer use.
The team
Capacity Engineering
Stack
PythonSQLBigQueryKubernetesPrometheusPromQLGrafanaAWS
What you'll do
- Build capacity planning and allocation tools for teams, leadership, and schedulers.
- Develop pipelines that ingest and normalize fleet utilization, occupancy, and cost data into BigQuery.
- Improve workload utilization through instrumentation, benchmarking, baselines, and collaboration with system-owning teams.
- Reconcile cloud billing with telemetry and internal systems; attribute spend and forecast compute demand.
- Operate Kubernetes-native collection, labeling, reservation, and scheduling systems at scale.
- Define data contracts, monitoring, on-call practices, and SLOs for internal data products.
What we're looking for
- Track record building and operating production systems in hands-on engineering roles.
- Production-quality Python and SQL skills.
- Deep operational experience with at least one major cloud provider: AWS, Google Cloud, or Azure.
- Experience with Prometheus, PromQL, and Grafana, including recording rules and production monitoring.
- Ability to gather requirements and collaborate across teams in an ambiguous environment.
Nice to have
- Experience with capacity planning, resource management, or cost attribution at large scale.
- Scheduling, packing efficiency, or profiling-driven optimization of distributed workloads.
- Multi-cloud data ingestion and cloud billing normalization experience.
- Experience with total cost of ownership analysis and infrastructure forecasting.
- Familiarity with GPU, TPU, or Trainium infrastructure and machine learning workloads.
- Experience building discoverable, self-service internal data products.
- Storage efficiency, retention, or lifecycle programs at exabyte scale.
Benefits
- Flexible working hours
- Generous vacation and parental leave
- Optional equity donation matching
- Hybrid work; staff are generally expected in an office at least 25% of the time