Network Engineer, Capacity and Efficiency

Network Engineer, Capacity and Efficiency 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.

The Capacity & Efficiency team sits inside Anthropic’s Compute organization and owns the cost, utilization, and attribution story for non‑accelerator infrastructure—the network, compute, and storage backbone that moves petabytes between training clusters, inference fleets, and object storage across clouds and regions. Anthropic runs a private multi‑cloud backbone built from dark fiber, optical transport, and CSP direct‑connect products, layered over data‑center fabrics spanning tens of thousands of hosts.

The scale is real, the spend is large, and the efficiency levers are still mostly unpulled.

We work alongside the Systems Networking team (who build and operate the fabric) and the Observability team (who own the telemetry platform). This role lives at the intersection: you’ll use deep networking knowledge and rigorous measurement to figure out where and how bandwidth, latency, and dollars are being used, find optimization opportunities and land them.

We’re looking for a network engineer who thinks in metrics first. You understand spine‑leaf fabrics, BGP, SDN overlays, and cloud interconnect products well enough to build them. You will instrument them, model their cost-per‑bit, and squeeze out the inefficiency, while ensuring we can move the bits to the right places in the most efficient manner. You’ll own the observability and efficiency surface for Anthropic’s network: from per‑flow telemetry on backbone routers, to QoS policy on cross‑region links carrying inference traffic, to cost attribution that tells a research team exactly what their checkpoint sync is costing.

This is a hands‑on IC role. You’ll write code (Python, Go), build dashboards, model capacity, and ship config changes to production routers. You’ll also influence architecture: when the data says a traffic pattern is pathological, you’ll be in the room causing it and fixing it.

You will be working across three areas: network telemetry and observability, traffic engineering, and cost modeling and attribution. We expect you to be strong in at least two and willing to grow into the third. If you're a telemetry‑first engineer who's never built a chargeback model, or a traffic engineer who hasn't shipped eBPF probes, apply anyway and tell us which axis you want to grow on.

Build the network observability stack. Design and deploy telemetry pipelines—sFlow/IPFIX, gNMI streaming, eBPF host probes—that turn packet counters into per‑flow, per‑tenant, per‑workload cost and utilization data. Own the SLIs for backbone and DCN fabric health.

Hunt for efficiency. Analyze inter‑region traffic patterns, identify hot links and stranded capacity, and quantify the dollar impact. Build the models that tell us whether we should buy more capacity, or move the workload.

Own QoS and traffic engineering. Design and operate traffic classification, marking, and shaping across the backbone. Make sure bulk checkpoint transfers don’t starve latency‑sensitive inference, and that we’re not paying premium cross‑region rates for traffic that could take the cheap path.

Drive cost attribution. Tie network spend—egress, interconnect ports, transit, optical leases—to the teams and workloads that generate it. Make network cost a first‑class input to capacity planning and workload placement decisions.

• Influence decisions you don’t own. A large fraction of this role is convincing other teams to act on what your data shows: making the case to research that a traffic pattern needs to change, to finance that an interconnect tranche is worth buying, to Systems Networking that a QoS policy needs rewriting. You’ll partner closely with Systems Networking on fabric architecture and Observability on telemetry platform integration, but the cost and efficiency wins will…