Engineering Manager, Inference Routing and Performance

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 Every request that hits Claude — from claude.ai , the API, our cloud partners, or internal research — passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet. Get it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed. The Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet — the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time. As Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team. The engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators. They work shoulder-to-shoulder with teams that write kernels and ML framework internals. The EM for this team doesn't need to write kernels — but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet. You'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence. The job is holding both. Representative work: Things the Inference Routing EM actually spends time on: Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers Working through a persistent tail-latency regression with the team — walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep What you'll do: Drive system-level performance Own the technical roadmap for cluster-level inference efficiency — routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is Deliver reliably and operate cleanly Set technical strategy for how routing evolves across heterogene ... (truncated, view full listing at source)