Anthropic
Staff+ Software Engineer, Capacity Engineering
$320K–$485K
San Francisco, CA +3 · Staff · Full-time · Posted Jul 14, 2026
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
San Francisco, California, United States - Experience
- Years required correlate with internal job level requirements; no specific number stated.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field of study demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic says it will make every reasonable effort to obtain a visa and provides immigration-lawyer support.
About the job
Build and operate production systems for AI fleet capacity planning, allocation, utilization, cost attribution, forecasting, and telemetry data across cloud providers.
What you'll do
- Build capacity planning, allocation, monitoring, and scheduling systems for fleet operations.
- Improve hardware utilization and own cost attribution and compute forecasting across providers.
- Develop and operate data pipelines and Kubernetes-native systems, with responsibility for data quality, latency, on-call, and SLOs.
What they ask for
- Track record building and operating production systems.
- Production-quality Python and SQL skills.
- Deep operational experience with at least one major cloud provider: Amazon Web Services, Google Cloud, or Microsoft Azure.
- Experience with Prometheus, PromQL, and Grafana, including recording rules and monitoring.
Nice to have
- Experience in capacity planning, resource management, cost attribution, scheduling, or optimization of large-scale distributed workloads.
- Multi-cloud data ingestion, forecasting, accelerator infrastructure, or internal data products experience.
Pay
$320,000 — $485,000 USD base salary
Tools
Python, SQL, BigQuery, Kubernetes, Amazon Web Services, Google Cloud, Microsoft Azure, Prometheus
AI focus
Supports AI infrastructure by managing capacity and measuring utilization for training, inference, and evaluation workloads; no AI model development is described.
Model-written summary · the original posting is authoritative