About the role
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E , backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.
THE ROLE We’re hiring a Capacity and Infrastructure Analytics Lead to help Baseten build the analytics foundation for tracking infrastructure usage, capacity, and cloud spend across our growing AI inference platform. This is a foundational analytics role focused on understanding how compute resources are consumed, priced, and optimized across Baseten’s fleet. You’ll create reliable data models that bring together cloud billing exports, provider usage data, capacity data, and internal infrastructure telemetry into a unified view of cost and utilization. You’ll work closely with Finance, Infrastructure, Product, and Operations to answer questions like: How efficiently are we using committed capacity? How should we forecast infrastructure needs as usage grows?
RESPONSIBILITIES Build, enhance, and maintain dashboards that track cloud cost, usage, capacity, utilization, and infrastructure efficiency across Baseten’s fleet. Ingest, clean, and model billing and usage data from multiple cloud and infrastructure providers, including sources such as cost and usage reports, provider APIs, invoices, and internal infrastructure systems. Create canonical data models for capacity and usage across a variety of dimensions and time grains. Partner with Infrastructure Engineering and Finance to define core metrics for cloud spend, committed capacity utilization, cost allocation, unit economics, and infrastructure efficiency. Support forecasting and planning workflows by modeling historical usage, capacity trends, and infrastructure demands. Develop alerting for anomalies in billing, tagging, utilization, and capacity data. Ensure data reliability through testing, documentation, version control, and clear ownership across the analytics stack.