Engineering Manager, Inference Infrastructure

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 — 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. 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. 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. Key responsibilities Own the technical roadmap for how the inference fleet is coordinated — where traffic goes, where capacity lives, how caches are placed, how fast the system reacts to demand, and the protocols that keep the control plane and the inference engines in sync Partner with the product, inference engine, performance, and capacity teams to identify throughput, latency, utilization, and cost wins, then turn those into shipped improvements with measurable results Build the group's habit of quantitative modeling: claim a win only when you can measure it, and know before you ship what the expected effect is Set technical strategy for how the control plane evolves across heterogeneous hardware, across multiple cloud providers, and across all our serving surfaces Run the group's operational backbone — on-call rotations, incident response, postmortem review, deploy safety — so the teams can ship aggressively without the system becoming fragile Create clarity at a seam: this group sits between the API surface, the inference engines, capacity planning, and the cloud deployment teams Develop and retain strong existing teams, and hire against a high technical bar Coach engineers through a roadmap where priorities shift Shape team structure as the scope grows: decide where the boundaries between problem areas should sit, and grow leads who can own each Pick up slack when it matters. These are small teams on a critical path; sometimes the EM is the one unblocking a stuck initiative or synthesizing a design debate Minimum qualifications Engineering management experience leading teams on critical-path production infrastructure at scale A deep systems background — load balancing, scheduling, cluster orchestration, autoscaling, cache-coherent distributed state, high-performance networking, or similar — with enough depth to make architectural calls about how a large fleet is coordinated and to evaluate candidates who go to the kernel and framework level Experience shipping performance or efficiency improvements in large-scale systems, and the ability to explain, with numbers, what the impact was — including the cost side, not just the latency side Experience running production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline A results-oriented, impact-driven approach, and comfort working in a space where throughput, latency, cost, stability, launch timelines, and feature velocity all pull in different directions Ability to build strong relationships across team boundaries — this is a seam role, and much of the job is making sure other teams can rely on yours Curiosity about machine learning systems — you don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems Preferred qualifications 5+ years of engineering management experience Experience with LLM inference serving — KV caching, continuous batching, request scheduling, prefill/decode disaggregation Background in cluster schedulers, autoscalers, load balancers, service meshes, or fleet control planes at scale (Kubernetes internals, Borg-style systems, or equivalents) Experience running worklo