Staff Software Engineer, Observability & Profiling

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 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, profiling, 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—and an increasing share of the hardest problems live below the application layer. We're building next-generation observability systems—high-throughput telemetry pipelines, fleet-wide continuous profiling, eBPF-based tracing and network visibility, and agentic diagnostic tools—so engineers can detect, diagnose, and resolve issues in minutes rather than hours, even when the answer lies in the kernel, the network stack, or an accelerator rather than on a dashboard.

Key responsibilities

• Design and build scalable telemetry ingest and storage pipelines for metrics, logs, traces, and error data across Anthropic's multi-cluster infrastructure
• Build observability solutions that give engineers deep, low-overhead visibility into system behavior across the fleet
• 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 eBPF-based auto-instrumentation to emit high-quality telemetry, with and without code changes
• Reduce mean time to detection and resolution by building cross-signal correlation—from kernel-level events up to application traces—unified query interfaces, and AI-assisted diagnostic tooling
• Drive fleet-wide efficiency by turning continuous profiling and utilization telemetry into actionable optimization insights across CPU, memory, and accelerator fleets
• Partner with Research, Inference, Product, and Infrastructure teams to ensure observability solutions meet the unique needs of each organization

Minimum qualifications

• Have hands-on experience building and operating large-scale observability or monitoring infrastructure
• Have deep, hands-on experience with observability signals end to end—from instrumentation through ingest to query and analysis
• Understand high-throughput telemetry pipelines and the tradeoffs involved in collecting, storing, and querying operational data at scale
• Are comfortable digging below the application layer into the kernel, the network stack, or the hardware
• 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 comfortable navigating ambiguous, high-impact technical challenges, both independently and with a team

Preferred qualifications

• 10+ years of relevant industry experience, including building and operating large-scale observability or monitoring infrastructure
• Experience building or operating eBPF-based observability in production—tracing, profiling, or network visibility
• Experience running continuous profiling at fleet scale, including managing overhead budgets and symbolization
• Kernel- and syscall-level debugging experience and performance engineering craft
• Experience profiling or instrumenting accelerator workloads
• Experience operating metrics systems at very high cardinality, or large-scale telemetry storage backends
• Experience with OpenTelemetry instrumentation, collector pipelines, and tail-based sampling strategies
• Interest in applying AI/LLMs to operational workflows such as automated