Principal Engineer Capacity/Compute

Anthropic•New York City, NY

4d•Hybrid

About The Position

Anthropic’s Capacity Engineering team ensures its large, rapidly growing infrastructure fleet is accounted for, well utilized, and efficiently allocated across accelerator and CPU families and cloud providers. The team builds the data, tooling, and operational systems used to plan, measure, and maximize utilization across first-party and third-party compute. This hands-on engineering role builds production systems including pipelines for heterogeneous cloud telemetry, fleet observability tooling, and performance instrumentation for workloads. The engineer will work with Kubernetes-native infrastructure at scale and influence decisions about infrastructure spending. The role involves collaboration with research engineering, infrastructure, inference, and finance teams in a high-autonomy, ambiguous environment. It is a pipeline role spanning data platform, planning, efficiency, and attribution and forecasting; focus may depend on background and business priorities, and the areas overlap. The position includes building planning and allocation tools, driving utilization and efficiency programs, reconciling provider billing with telemetry and internal systems, forecasting compute needs, and developing BigQuery-backed data products. It also involves operating Kubernetes-native systems, establishing data quality and latency objectives, gathering requirements, designing schema contracts, and supporting internal consumers from research engineers to finance leadership. The role is hybrid under Anthropic’s location-based policy: staff are currently expected to work from an office at least 25% of the time, although some roles may require more. Minimum education is a bachelor’s degree or equivalent combination of education, training, and/or experience, in a field relevant to the role as demonstrated through coursework, training, or professional experience. Years of experience required correlate with the internal job level. Annual salary range: $320,000–$485,000 USD.

Requirements

Strong track record building and operating production systems.
Production-quality Python and SQL skills; pipeline code is primarily Python, and the presentation layer uses BigQuery SQL, including table-valued functions and views.
Deep experience with at least one major cloud provider: Amazon Web Services, Google Cloud, or Microsoft Azure, including its operations.
Experience with observability tooling including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring relied upon 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/or experience.
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 or developer experience.
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 different provider billing arrangements.
Total cost of ownership and forecasting experience, including analyzing whether infrastructure growth is causal or correlated with business drivers.
Familiarity with accelerator infrastructure, including GPU metrics such as 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 involving stranding and rightsizing, recovery of unused capacity, and job-level utilization across training, inference, and evaluation systems.
Establish per-configuration performance baselines and work with system-owning teams to improve utilization.
Reconcile billing across more than ten providers against telemetry and internal systems, attribute spending to workloads, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline.
Build data pipelines that ingest occupancy, utilization, and cost data from a diversifying fleet into BigQuery, owning completeness, latency SLOs, and gap detection.
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 including research engineers and finance leadership.
Support load-bearing data and operational surfaces with on-call coverage and service-level objectives.