Principal Engineer Capacity/Compute

New York City, NY · San Francisco · USD 320,000-485,000 / year · Hybrid · Posted Oct 19, 2026

At a glance

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

Anthropic is seeking a Principal Engineer for Capacity/Compute to join a rapidly growing infrastructure fleet spanning multiple accelerator families, CPU families, and clouds.
The Capacity Engineering team owns the data, tooling, and operational systems that allow Anthropic to plan, measure, and maximize utilization across first-party and third-party compute.

Build the planning and allocation stack including tools used by leadership to allocate capacity, teams to plan against allocations, and the scheduler to enforce them.
Drive efficiency programs focused on stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval.
Own attribution and forecasting by reconciling billing across ten-plus providers against telemetry and internal systems, attributing spend to workloads, and turning demand signals into a defensible compute plan and supply pipeline.
Build the underlying data platform with pipelines ingesting occupancy, utilization, and cost from a diversifying fleet into BigQuery while owning completeness, latency SLOs, and gap detection.
Operate Kubernetes-native systems at scale including collection agents, workload labeling, and taint, reservation, and scheduling behavior.
Treat output as a product by gathering requirements, defining schema contracts, and designing for consumers ranging from research engineers to the CFO.

A strong track record building and operating production systems with a DevOps flavor.
Proficiency in Python and SQL at production quality, including BigQuery SQL, table-valued functions, and views.
Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations.
Experience with an observability tooling stack, including Prometheus, PromQL, and Grafana, such as writing recording rules and monitoring.
Ability to gather own requirements and work across organizational boundaries in an ambiguous environment with limited direction.
Bachelor's degree or an equivalent combination of education, training, and/or experience in a relevant field of study.

Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment.
Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads.
Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry.
Total cost of ownership and forecasting experience, including decomposing infrastructure growth.
Accelerator infrastructure familiarity such as GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level.
Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability.
Storage efficiency, retention, and lifecycle program experience at exabyte scale.

Location-based hybrid policy requiring staff to be in one of the offices at least 25% of the time, though some roles may require more time in offices.