Engineering Manager, Inference Infrastructure

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><p><strong>About the role</strong></p> <p>Every request that hits Claude — from <a href="http://claude.ai" target="_blank">claude.ai</a>, the API, our cloud partners, or internal research — depends on a set of decisions made before it ever reaches a model: where each request should be served and how much capacity each model needs right now. Getting those decisions right is crucial to satisfying throughput, reliability, and latency constraints. This group builds the control plane that makes those decisions for Anthropic's inference fleet and own the inference request path.</p> <p>This is a deeply technical group. The engineers here design placement and load-balancing algorithms, build quantitative models of demand, capacity, and system performance, improve latency across kernel, network, and framework boundaries, and reason carefully about how a change to the fleet ripples through everything that depends on it.</p> <p>You'll lead a strong group of ML platform, infrastructure, and distributed-systems engineers working alongside the teams that build our ML internals and cloud infrastructure. You need enough systems depth to make architectural calls, hire people who go deep, and see when a proposed change will ripple across the fleet. You're accountable for the health of the whole path from request to model: its efficiency, its reliability, and how well it evolves as models, hardware, and clouds change underneath it.</p> <p><strong>Key responsibilities</strong></p> <ul> <li>Own the technical roadmap for how the inference fleet is coordinated — where traffic goes, where capac