About this role
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the Role
Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, CPU families, and clouds. The Capacity Engineering team is responsible for making sure all of our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute — one of the company's largest areas of spend.
As the Senior Engineering Manager for Capacity Engineering, you will lead the team that builds and operates these production systems. You'll set technical direction, grow and develop a team of senior and staff-level engineers, and be accountable for the reliability and correctness of surfaces that leadership, research engineering, inference, infrastructure, and finance all depend on. This is a hands-on leadership role: we expect you to stay close enough to the systems to review designs, make sound architectural calls, and step into an incident when the team needs you — while spending most of your time on people, priorities, and cross-organizational alignment.
The team's work spans three overlapping areas, and you'll be responsible for balancing investment across them as business priorities shift:
• Data platform — Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Consumers range from research engineers to finance to leadership, so this is product work as much as engineering.
• Planning and Assurance — Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation.
• Efficiency — Measuring and improving how effectively every major workload uses the hardware it runs on, across training, inference, and evals. Building benchmarking infrastructure and per-config baselines, then partnering with system-owning teams to close the gaps.
Key Responsibilities
• Be hands-on, lead and grow the team. Hire, onboard, coach, and retain senior and staff engineers. Set clear expectations, give direct and timely feedback, run performance and leveling conversations, and build a team culture that values ownership, rigor, and collaboration.
• Champion your internal customers. We build for our own use cases, so the teams that depend on our systems — research engineering, inference, infrastruct