Principal Engineer Capacity/Compute (AI)

Location: San Francisco, CA; hybrid, with staff expected to be in one of the offices at least 25% of the time. Some roles may require more office time.

Annual salary: $320,000–$485,000 USD.

Company

Anthropic builds reliable, interpretable, and steerable AI systems intended to be safe and beneficial for users and society.

What you will do

• Build planning and allocation systems for cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs, and scheduler enforcement.
• Drive efficiency programs covering stranding, rightsizing, unused-capacity recovery, and job-level utilization across training, inference, and evaluation workloads.
• Own attribution and forecasting by reconciling billing from more than ten providers with telemetry and internal systems, attributing spend, and creating defensible compute plans.
• Build and operate data pipelines that ingest occupancy, utilization, and cost data into BigQuery, including completeness, latency SLOs, gap detection, and new-provider integrations.
• Operate Kubernetes-native collection, labeling, taint, reservation, and scheduling systems at scale.
• Gather requirements, define schema contracts, design self-service data products, and own operational quality through on-call and SLOs.

Requirements

• Strong track record building and operating production systems in a hands-on engineering role with a DevOps focus.
• Production-quality, idiomatic, well-tested, and maintainable Python and SQL, including BigQuery table-valued functions and views.
• Deep experience operating at least one major cloud provider: Amazon Web Services, Google Cloud, or Microsoft Azure.
• Experience with observability tooling including Prometheus, PromQL, and Grafana, including recording rules and monitoring relied on by engineering teams.
• Ability to gather requirements independently and work across organizational boundaries in an ambiguous environment with limited direction.
• Bachelor’s degree or an equivalent combination of education, training, and experience; relevant field of study demonstrated through coursework, training, or professional experience.
• Years of experience must correspond to the internal job-level requirements.

Nice to have

• Capacity planning, resource management, or cost attribution experience 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 billing exports, reservation APIs, capacity reservations, commitments, and vendor telemetry.
• Total cost of ownership and forecasting experience, including separating infrastructure growth caused by business drivers from correlated growth.
• Accelerator infrastructure experience with GPU metrics such as DCGM, TPU utilization, Trainium power and utilization, or hardware-level machine learning training and inference systems.
• Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability.
• Storage efficiency, retention, and lifecycle experience at exabyte scale.

Culture & Benefits

• Collaborative work across research engineering, infrastructure, inference, finance, policy, and business teams.
• Flexible working hours and an office environment for collaboration.
• Competitive compensation, optional equity donation matching, generous vacation, and parental leave.
• Work focused on large-scale AI research and the development of trustworthy, steerable AI systems.
• Visa sponsorship may be available; Anthropic makes reasonable efforts to support visas and retains an immigration lawyer.