Описание

This role at Anthropic involves building production data pipelines, observability systems, and capacity planning tooling for large-scale compute fleets, focusing on making infrastructure resources accounted for, well-utilized, and efficiently allocated.

Responsibilities

Build the planning and allocation stack including tools for leadership to allocate capacity, teams to plan against allocations, and the scheduler to enforce them
Drive efficiency programs such as stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and evaluation
Own attribution and forecasting, reconciling billing across multiple providers, attributing spend, and turning demand signals and research roadmaps into a defensible compute plan
Build the data platform, including pipelines ingesting occupancy, utilization, and cost from a diversifying fleet into BigQuery, with ownership of completeness, latency SLOs, and gap detection
Operate Kubernetes-native systems at scale, including collection agents, workload labeling, and scheduling behavior
Treat the output as a product, gathering requirements, defining schema contracts, and designing for various consumers from research engineers to a CFO, including on-call and SLOs

Requirements

Strong track record building and operating production systems
Production-quality Python and SQL skills; pipeline code in Python, presentation layer in BigQuery SQL (table-valued functions, views)
Deep experience with at least one major cloud provider (AWS, Google Cloud, or Azure) and its operations
Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, and building reliable monitoring
Ability to gather own requirements and work across organizational boundaries in an ambiguous environment with limited direction

Nice to have

Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment
Experience with scheduling and packing efficiency, or profiling-driven optimization of large distributed workloads
Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, and vendor telemetry from different providers
Total cost of ownership and forecasting experience, including decomposing infrastructure growth
Accelerator infrastructure familiarity (GPU metrics, TPU utilization, Trainium power and utilization, machine learning training/inference systems at 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

We offer

Hybrid work in Seattle, WA, with an expectation to be in the office at least 25% of the time
Annual salary range of $320,000 to $485,000 USD
Visa sponsorship is available
Competitive compensation and benefits
Optional equity donation matching
Generous vacation and parental leave
Flexible working hours
Collaborative office space

Hiring process

Apply even if you do not believe you meet every single qualification