Description
About the Role
Anthropic's Accelerator Capacity Engineering (ACE) team manages one of the largest and fastest-growing accelerator fleets in the industry. As an engineer on ACE, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on.
What This Team Owns
The team’s work spans three functional areas: data infrastructure, fleet observability, and compute efficiency. Depending on your background and interests, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap:
Data Infrastructure
Collecting, normalizing, and serving the fleet-wide data that powers everything else. This means building pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage data across cloud providers, and maintaining the BigQuery layer that the rest of the org queries against.
Fleet Observability
Making the state of the accelerator fleet legible and actionable in real time. This means building cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and driving systemic improvements to scheduling and fragmentation.
Compute Efficiency
Measuring and improving how effectively every major workload uses the hardware it’s running on. This means instrumenting utilization metrics across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and collaborating directly with system-owning teams to close efficiency gaps.
What You’ll Do
- Build and operate data pipelines that ingest accelerator occupancy, utilization, and cost data from multiple cloud providers into BigQuery.
- Develop and maintain observability infrastructure , Prometheus recording rules, Grafana dashboards, and alerting systems , that surface actionable signals about fleet health, occupancy, and efficiency.
- Instrument and analyze compute efficiency metrics across training, inference, and eval workloads.
- Build internal tooling and platforms that enable capacity planning, workload attribution, and cluster debugging.
- Operate Kubernetes-native systems at scale , deploying data collection agents, managing workload labeling infrastructure, and understanding how taints, reservations, and scheduling affect capacity.
- Normalize and reconcile data across heterogeneous sources , including AWS, GCP, and Azure billing exports, vendor-specific telemetry formats, and internal systems with different schemas and billing arrangements.
You May Be a Good Fit If You Have
- 5+ years of software engineering experience with a strong track record building and operating production systems.
- Kubernetes fluency at operational depth , you’ve operated production K8s at meaningful scale, not just written manifests.
- Data pipeline engineering experience , designing, building, and owning the full lifecycle of production data pipelines.
- Observability tooling experience , Prometheus, PromQL, and Grafana are in the critical path for this team.
- Python and SQL at production quality.
- Familiarity with at least one major cloud provider (AWS, GCP, or Azure) at the infrastructure level , compute, billing, usage APIs, cost management tooling.
Strong Candidates May Also Have
- Multi-cloud data ingestion experience , especially working with AWS and GCP APIs, billing exports, or vendor-specific telemetry formats.
- Accelerator infrastructure familiarity , GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience working with ML training/inference systems at the hardware level.
- Performance engineering and benchmarking experience , building benchmark harnesses, establishing baselines, reasoning about compute efficiency (FLOPs utilization, memory bandwidth, interconnect throughput), and working with system teams to diagnose and improve performance.
- Data-as-product thinking , experience building internal data products with self-service access, schema contracts, API serving, documentation,