Principal Engineer Capacity/Compute

Anthropic • New York, NY

5d • $320,000 - $485,000 • Hybrid

About The Position

Anthropic’s Capacity Engineering team ensures that infrastructure resources across accelerator and CPU families and cloud providers are accounted for, well utilized, and efficiently allocated. The team owns the data, tooling, and operational systems used to plan, measure, and maximize utilization across first-party and third-party compute.

The Principal Engineer will build production systems for this work, including telemetry pipelines for heterogeneous cloud environments, observability tooling for real-time fleet health, and performance instrumentation to measure workload efficiency. The role involves writing production-quality code, operating Kubernetes-native infrastructure at scale, and influencing decisions about a major area of company spending. It requires moving comfortably between data engineering, systems engineering, and observability in a high-autonomy, ambiguous environment, while working with research engineering, infrastructure, inference, and finance teams.

This pipeline role feeds four overlapping areas: data platform, planning, efficiency, and attribution and forecasting. Focus will depend on the candidate’s background and business priorities. The work includes building data products and planning tools, improving workload and fleet utilization, reconciling cloud costs with telemetry and internal systems, and turning demand signals and research roadmaps into defensible compute plans. The role also includes gathering requirements, defining schema contracts, designing for varied consumers, and owning operational concerns such as on-call and SLOs.

The position requires a bachelor’s degree or equivalent combination of education, training, and experience in a relevant field. The role is hybrid under Anthropic’s current policy, with staff expected to be in an office at least 25% of the time, though some roles may require more. The annual salary range is $320,000–$485,000 USD.

Requirements

• A strong track record building and operating production systems; this is a hands-on engineering role with a DevOps flavor.
• Production-quality Python and SQL skills, including idiomatic, well-tested, and maintainable code; SQL work includes 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 writing recording rules and building monitoring relied on by engineering teams.
• Ability to gather requirements independently and work across organizational boundaries in an ambiguous environment with limited direction.
• A bachelor’s degree or equivalent combination of education, training, and experience.
• A relevant field of study, demonstrated through coursework, training, or professional experience.

Nice To Haves

• Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment; product engineering and developer experience are also relevant.
• 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, on-demand capacity reservations, commitments, and vendor telemetry across providers with different billing arrangements.
• Experience with total cost of ownership and forecasting, including determining whether infrastructure growth is causal or correlated with business drivers.
• Familiarity with accelerator infrastructure, such as GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or 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.
• Experience with storage efficiency, retention, and lifecycle programs at exabyte scale.

Responsibilities

• Build the planning and allocation stack used by leadership to allocate capacity, teams to plan against allocations, and the scheduler to enforce allocations, including cross-region and cross-provider placement, guardrails, queueing, and occupancy KPIs.
• Drive efficiency programs covering stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and evaluation workloads.
• Establish per-configuration utilization baselines and work with system-owning teams to improve efficiency.
• Reconcile billing across more than ten providers against telemetry and internal systems, attribute spending to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline.
• Build and operate data pipelines that ingest occupancy, utilization, and cost data from a diversifying fleet into BigQuery, with ownership of completeness, latency SLOs, and gap detection.
• Integrate new cloud and infrastructure providers into the data platform.
• Operate Kubernetes-native systems at scale, including collection agents, workload labeling, and taint, reservation, and scheduling behavior that determines usable capacity.
• Gather requirements, define schema contracts, and design data products for consumers ranging from research engineers to finance leadership.
• Own operational readiness of load-bearing data and observability surfaces, including on-call and SLOs.

Benefits

• Competitive compensation
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Office space for collaboration
• Visa sponsorship, with reasonable efforts to support candidates who receive an offer

What This Job Offers

• Job Type: Full-time
• Career Level: Principal