Staff+ Software Engineer, Observability

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 is seeking talented and experienced Software Engineers to join our Observability team within the Infrastructure organization. The Observability team owns the monitoring and telemetry infrastructure that every engineer and researcher at Anthropic depends on—from metrics and logging pipelines to distributed tracing, error analytics, alerting, and the dashboards and query interfaces that make it all actionable. By joining this team, you’ll have a direct impact on the reliability and operational excellence of Anthropic’s research and product systems. As Anthropic scales its infrastructure across massive GPU, TPU, and Trainium clusters, the volume and complexity of operational data is growing by orders of magnitude. We’re building next-generation observability systems—high-throughput ingest pipelines, cost-efficient columnar storage, unified query layers across signals, and agentic diagnostic tools—to ensure that engineers can detect, diagnose, and resolve issues in minutes rather than hours, even as the systems they operate become exponentially more complex. Responsibilities Design and build scalable telemetry ingest and storage pipelines for metrics, logs, traces, and error data across Anthropic’s multi-cluster infrastructure Own and evolve core observability platforms, driving migrations and architectural improvements that improve reliability, reduce cost, and scale with organizational growth Build instrumentation libraries, SDKs, and integrations that make it easy for engineering teams to emit high-quality telemetry from their services Drive alerting and SLO infrastructure that enables teams to define, monitor, and respond to reliability targets with minimal noise Reduce mean time to detection and resolution by building cross-signal correlation, unified query interfaces, and AI-assisted diagnostic tooling Partner with Research, Inference, Product, and Infrastructure teams to ensure observability solutions meet the unique needs of each organization You May Be a Good Fit If You Have 10+ years of relevant industry experience building and operating large-scale observability or monitoring infrastructure Have deep experience with at least one observability signal area (metrics, logging, tracing, or error analytics) and familiarity with the others Understand high-throughput data pipelines, columnar storage engines, and the tradeoffs involved in ingesting and querying telemetry data at scale Have experience operating or building on top of observability platforms such as Prometheus, Grafana, ClickHouse, OpenTelemetry, or similar systems Have strong proficiency in at least one of Python, Rust, or Go Have excellent communication skills and enjoy partnering with internal teams to improve their operational visibility and incident response capabilities Are excited about building foundational infrastructure and are comfortable working independently on ambiguous, high-impact technical challenges Strong Candidates May Also Have Experience operating metrics systems at very high cardinality (hundreds of millions of active time series or more) Experience with log storage migrations or operating columnar databases (ClickHouse, BigQuery, or similar) for analytics workloads Experience with OpenTelemetry instrumentation, collector pipelines, and tail-based sampling strategies Experience building or operating alerting platforms, on-call tooling, or SLO frameworks at scale Experience with Kubernetes-native monitoring, eBPF-based observability, or continuous profiling Interest in applying AI/LLMs to operational workflows such as automated root cause analysis, anomaly detec ... (truncated, view full listing at source)