Senior Engineering Manager, Capacity Engineering

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 and will be onsite 5 days a week: 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 sustainable.
• Run the team as a product organization. Ensure the team gathers its own requirements, defines schema contracts, and designs for a wide range of consumers — from research engineers to a CFO. Treat data quality and discoverability as first-class deliverables.
• Be the primary partner for cross-functional stakeholders. Work closely with infrastructure, inference, research engineering, and finance leadership to align on capacity decisions, efficiency targets, and spend. Represent the team's data and recommendations to senior leadership.
• Drive operational excellence. Own reliability and incident response for load-bearing systems, establish SLOs and on-call practices, and continuously reduce operational toil so the team can spend its time on higher-leverage work.
• Scale the function. As the fleet diversifies (every new provider is a net-new integration), anticipate where the team needs to grow in headcount, skills, and systems — and make the case for it.

• Experience managing software or infrastructure engineering teams, including hiring senior engineers, managing performance, and developing people into larger scope.
• A strong technical background in production systems — data engineering, infrastructure, distributed systems, or observability — with hands-on experience you can still draw on when reviewing designs or debugging with the team.
• Familiarity with at least one major cloud provider (AWS, GCP, or Azure), Kubernetes-based infrastructure, and modern observability stacks (e.g., Prometheus, Grafana).
• A track record of setting and executing an engineering roadmap in an ambiguous, high-autonomy environment with many stakeholders and shifting priorities.
• Excellent communication skills: you can explain a utilization metric to a research engineer and a spend forecast to a CFO, and you can advocate clearly for your team's priorities with senior leadership.
• Comfort owning operational responsibility for systems the company depends on, including on-call and incident management.

• Experience leading teams working on capacity planning, resource management, product engineering or FinOps at a hyperscaler or in a large-scale ML environment.
• Familiarity with accelerator infrastructure — GPU metrics (DCGM), TPU utilization, or ML training and inference systems at the hardware level.
• Experience with multi-cloud billing and telemetry normalization (billing exports, reservation APIs, commitments, on-demand capacity reservations).
• Experience building or leading internal data products with self-service access, schema contracts, and documentation.
• Background in scheduling, packing efficiency, or profiling-driven optimization of large distributed workloads.