# Engineering Manager, Inference Infrastructure at Anthropic

- Company: Anthropic
- What the company does: Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Backed by Accel, Bessemer and General Catalyst.
- Company website: https://www.anthropic.com/
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco, CA | New York City, NY | Seattle, WA
- Work setup: On-site
- Posted: 2026-09-01
- Apply by: 2026-10-16
- Apply: https://job-boards.greenhouse.io/anthropic/jobs/5411560008
- Page: https://www.1752.vc/careers/jobs/anthropic-engineering-manager-inference-infrastructure/

## 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.

## What they're looking for

- Engineering management experience leading teams on critical-path production infrastructure at scale
- 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

Tags: Software Engineering - Infrastructure