Anthropic
Staff+ Software Engineer, Observability
$320K–$485K
San Francisco, CA +4 · Staff · Full-time · Posted Mar 3, 2026
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
New York, New York, United States
San Francisco, California, United States - Experience
- Years correlate with the internal job level requirements; no specific number stated.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field of study as demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic will make every reasonable effort to obtain a visa and provides immigration-lawyer support.
About the job
Build and operate fleet-wide observability infrastructure that helps Anthropic teams monitor, diagnose, and optimize research and product systems.
What you'll do
- Design scalable telemetry pipelines and observability platforms, including instrumentation, storage, and query tools.
- Develop low-overhead fleet visibility, eBPF-based auto-instrumentation, cross-signal diagnostics, and profiling-based optimization.
- Lead platform improvements and partner with Research, Inference, Product, and Infrastructure teams.
What they ask for
- Experience building and operating large-scale observability or monitoring infrastructure.
- Hands-on experience with observability signals end to end, from instrumentation through ingest to query and analysis.
- Understanding of high-throughput telemetry pipelines and operational-data tradeoffs at scale.
- Ability to debug below the application layer, including in the kernel, network stack, or hardware.
Nice to have
- Production experience with eBPF-based observability or fleet-scale continuous profiling.
- Experience with accelerator workloads, high-cardinality metrics, telemetry storage, ClickHouse, BigQuery, or OpenTelemetry.
Pay
$320,000 — $485,000 USD (base salary)
Tools
eBPF, ClickHouse, BigQuery, OpenTelemetry
AI focus
Build AI-assisted diagnostic tools and apply AI/LLMs to operational workflows such as root-cause analysis, anomaly detection, and intelligent alerting.
Model-written summary · the original posting is authoritative