Senior Engineering Manager, Capacity Engineering

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.

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.

• 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, infrastructure, and finance — are your customers. Engage with them directly, bring what you learn back into the roadmap, and lead the team in building tools people genuinely want to use.

• Own the roadmap. Translate company-level compute strategy into a prioritized engineering roadmap across data platform, planning and efficiency. Make explicit trade-offs when priorities compete, and communicate them clearly upward and outward.

• Set the technical bar. Review designs, weigh in on architecture, and hold the team to production standards — well-tested Python and SQL, latency and completeness SLOs, gap detection, and on-call that is