Senior Observability Engineer - Fleet Profiling

EngineersOfAI; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Job summary

Anthropic is seeking Software Engineers for the Observability team within Infrastructure. You will design and operate high-throughput telemetry pipelines—metrics, logs, traces, and error data—across a multi-cluster environment, enabling quick detection, diagnosis, and resolution of issues.

You will own core observability platforms, drive architectural improvements to improve reliability, reduce cost, and scale with growth, while building instrumentation libraries and AI-assisted diagnostic

Qualifications

• Hands-on experience building and operating large-scale observability or monitoring infrastructure.
• End-to-end observability signals—from instrumentation through ingest to query and analysis.
• Comfort digging below the application layer into kernel, network stack, or hardware.

Responsibilities

• Design and build scalable telemetry ingest and storage pipelines for metrics, logs, traces, and error data across multi-cluster infrastructure.
• Own and evolve observability platforms, driving migrations and architectural improvements for reliability and cost.
• Build instrumentation libraries and eBPF-based auto-instrumentation to emit telemetry with/without code changes.
• Reduce MTTR by cross-signal correlation and unified query interfaces with AI-assisted tooling.
• Collaborate with Research, Inference, Product, and Infrastructure teams to meet diverse observability needs.

Skills

Observability infra

Telemetry signals

Kernel debugging

Communication skills

Tools

eBPF tooling

OpenTelemetry

Job description

Anthropic is seeking Software Engineers for the Observability team within Infrastructure. You will design and operate high-throughput telemetry pipelines—metrics, logs, traces, and error data—across a multi-cluster environment, enabling quick detection, diagnosis, and resolution of issues.

You will own core observability platforms, drive architectural improvements to improve reliability, reduce cost, and scale with growth, while building instrumentation libraries and AI-assisted diagnostic