Anthropic is seeking a Staff+ Software Engineer in Capacity Engineering to build and operate production systems that manage the company’s largest infrastructure fleet. The role focuses on building data pipelines, observability tooling, and performance instrumentation to optimize resource utilization across multiple cloud providers. Responsibilities include designing planning and allocation tools, driving efficiency initiatives, reconciling billing and forecasting, and operating Kubernetes-native systems at scale. The position requires strong experience in Python, SQL, cloud operations, observability stack, and capacity planning, with a proven track record of building production-quality systems. Successful candidates will work closely with research engineering, infrastructure, inference, and finance teams, and will shape the product for internal stakeholders ranging from engineers to finance leadership.
Staff+ Software Engineer, Capacity Engineering en Anthropic
Presencial - San Francisco, CA
Más vacantes en AnthropicRequirements
Skills
- Proven track record building and operating production systems with a devops focus
- Production-quality Python and SQL development
- 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 solutions
- Ability to gather requirements and work across organizational boundaries in an ambiguous environment
- Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or large‑scale machine‑learning environment
- Time spent in product engineering and developer experience
Responsibilities
- Build the planning and allocation stack tools used by leadership and teams, enabling cross‑region and cross‑provider placement, guardrails, queueing, and occupancy KPIs
- Drive efficiency programs such as stranding, rightsizing, unused capacity recovery, and job‑level utilization across training, inference, and evaluation workloads, establishing per‑config baselines and collaborating with system‑owning teams
- Own attribution and forecasting, reconciling billing across multiple providers with telemetry, attributing spend to workloads, and producing defensible compute plans and supply pipelines
- Build the data platform pipelines ingesting occupancy, utilization, and cost into BigQuery, ensuring completeness, latency SLOs, and gap detection while integrating new providers
- Operate Kubernetes‑native systems at scale, including collection agents, workload labeling, and taint/reservation/scheduling behavior that determines usable capacity
- Treat the output as a product rather than a pipeline, gathering requirements, defining schema contracts, and designing for diverse consumers from research engineers to CFOs, including on‑call and SLOs
Technologies
PythonSQLBigQueryPrometheusPromQLGrafanaKubernetesAWSGCPAzure
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