Staff+ Software Engineer (Capacity Engineering)

TL;DR

Staff+ Software Engineer (Capacity Engineering): Building and operating production systems to manage, plan, and optimize one of the industry's largest AI infrastructure fleets with an accent on data pipelines, observability, and resource allocation. Focus on designing Kubernetes-native systems, reconciling multi-cloud billing, and improving hardware utilization across training and inference workloads.

Location: Hybrid (Must be based in San Francisco, New York City, or Seattle; minimum 25% office presence required)

Salary: $320,000 - $485,000 USD

Company

Anthropic is a public benefit corporation dedicated to creating reliable, interpretable, and steerable AI systems.

What you will do

• Build the planning and allocation stack for capacity management, including cross-region and cross-provider placement and occupancy KPIs.
• Drive efficiency programs focused on rightsizing, unused capacity recovery, and job-level utilization across training and inference systems.
• Develop the underlying data platform using Python and BigQuery to ingest and normalize telemetry from heterogeneous cloud environments.
• Own attribution and forecasting, reconciling CSP billing exports against vendor telemetry and internal systems.
• Operate Kubernetes-native systems at scale, managing collection agents, workload labeling, and scheduling behavior.
• Treat internal data tools as products, defining schema contracts and designing for stakeholders ranging from engineers to the CFO.

Requirements

• Strong track record building and operating production systems with a DevOps flavor.
• Production-quality proficiency in Python and SQL (specifically BigQuery).
• Deep experience with at least one major cloud provider (AWS, GCP, or Azure) and its operations.
• Experience with the observability stack, including Prometheus, PromQL, and Grafana.
• Ability to operate in high-ambiguity environments and gather requirements independently.
• Bachelor's degree or equivalent professional experience in a relevant field.

Nice to have

• Experience with capacity planning or cost attribution at hyperscalers or in large-scale ML environments.
• Familiarity with accelerator infrastructure, including GPU (DCGM) or TPU utilization metrics.
• Experience normalizing multi-cloud billing exports and managing reservation APIs.
• Background in scheduling and packing efficiency or profiling-driven optimization of distributed workloads.
• Experience building internal self-service data products with schema contracts and API serving.

Culture & Benefits

• Collaborative "big science" research environment focusing on high-impact AI safety.
• Competitive compensation with optional equity donation matching.
• Generous vacation and parental leave policies.
• Flexible working hours and modern office spaces for collaboration.
• Visa sponsorship available for eligible candidates.