Staff Software Engineer, Observability & Profiling
About the role
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 root cause analysis, anomaly detection, or intelligent alerting
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:£325,000—£390,000 GBPLogistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
