Principal Engineer Capacity/Compute

Anthropic•Seattle, WA

4d•Hybrid

About The Position

Anthropic’s Capacity Engineering team ensures the company’s infrastructure resources are accounted for, well-utilized, and efficiently allocated across accelerator and CPU families and multiple cloud providers. The team owns the data, tooling, and operational systems used to plan, measure, and maximize utilization across first-party and third-party compute. The engineer will build and operate production systems including telemetry pipelines, fleet observability tooling, performance instrumentation, capacity planning and allocation platforms, and cost attribution and forecasting systems. The role spans data engineering, systems engineering, and observability, and involves operating Kubernetes-native infrastructure at scale. It also directly informs decisions about a major area of company spending. This is a pipeline role across data platform, planning, efficiency, and attribution and forecasting. The primary focus will depend on the candidate’s background and business priorities, with fluid boundaries and overlapping problems. The engineer will collaborate with research engineering, infrastructure, inference, and finance teams in a high-autonomy, high-ambiguity environment. The work includes gathering requirements, defining schema contracts, designing useful data products, and taking ownership of on-call responsibilities and SLOs. Anthropic expects staff to work from one of its offices at least 25% of the time under its current location-based hybrid policy. The annual salary range is $320,000–$485,000 USD. A bachelor’s degree or an equivalent combination of education, training, and/or experience is required; the field of study must be relevant to the role as demonstrated through coursework, training, or professional experience. Required years of experience correlate with the internal job level.

Requirements

A strong track record building and operating production systems.
Hands-on engineering experience with a DevOps-oriented approach.
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; writing recording rules; and building monitoring that engineering teams rely on.
Ability to gather 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 field relevant to the role, as 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 providers with different billing arrangements.
Experience with total cost of ownership and forecasting, including distinguishing whether infrastructure growth is causal or correlated with business drivers.
Familiarity with accelerator infrastructure, including 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 them.
Support cross-region and cross-provider placement, guardrails, queueing, and occupancy KPIs.
Drive efficiency programs, including stranding and rightsizing, recovery of unused capacity, and job-level utilization improvements across training, inference, and evaluation systems.
Establish per-configuration baselines and work with system-owning teams to improve utilization.
Own attribution and forecasting: reconcile billing across more than ten providers against telemetry and internal systems, attribute spend 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 rapidly 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 taint, reservation, and scheduling behavior that determines usable capacity.
Treat data outputs as products by gathering requirements, defining schema contracts, and designing for consumers ranging from research engineers to finance leadership.
Own on-call responsibilities and SLOs for load-bearing data and infrastructure surfaces.