Principal Engineer (Capacity/Compute)

Join Anthropic, a leading AI safety and research company, as a Principal Engineer in the Capacity Engineering team. You will be responsible for building production systems that optimize infrastructure resource utilization, improve efficiency, and drive cost attribution and forecasting. This role requires deep experience with cloud providers, production systems, and observability tooling. You will collaborate closely with various teams and have a direct impact on one of Anthropic's largest areas of spend.

Responsibilities

• Construire des systèmes de production qui alimentent le travail de l'équipe d'ingénierie de capacité, y compris des pipelines de données qui ingèrent et normalisent la télémétrie.
• Collaborer étroitement avec les équipes d'ingénierie de recherche, d'infrastructure, d'inférence et de finance pour définir les contrats de schéma et rendre les données découvrables.
• Mesurer et améliorer l'efficacité de l'utilisation des ressources matérielles par chaque charge de travail majeure, en établissant des références par configuration.
• Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements
• Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers

Requirements

• The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment
• Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations
• Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction
• Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable
• A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor
• Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed
• Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here
• Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with 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. Not just pipelines, but thinking about how the data gets consumed
• Storage efficiency, retention, and lifecycle program experience at exabyte scale

Preferred

• Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads

Pay

USD 320,000 - 485,000