# TIPM, AI Data Center Network Build, MLZ BaND at Amazon

- Company: Amazon
- What the company does: Backed by Kleiner Perkins and Madrona.
- Company website: http://www.amazon.com/
- Type: Startups (AI role)
- Level: Mid level
- Location: Herndon, Virginia, USA
- Work setup: On-site
- Posted: 2026-09-28
- Apply by: 2026-11-12
- Apply: https://www.amazon.jobs/en/jobs/10561597/tipm-ai-data-center-network-build-mlz-band
- Page: https://www.1752.vc/careers/jobs/amazon-tipm-ai-data-center-network-build-mlz-band/

## About the role

You will own network delivery end-to-end across a portfolio of US-wide data center builds, driving the full network lifecycle from early construction coordination through network general services, optical turnup, and production fabric readiness. You will work directly with network engineers, data center construction teams, optical design, and supply chain, cutting through ambiguity, resolving blockers, and keeping complex multi-stream programs moving in parallel.

## What they're looking for

- • 3+ years of technical program management experience in hyperscale infrastructure, network deployment, or data center construction environments
- • Hands-on familiarity with large-scale structured cabling systems: you can read a cut sheet, understand tray and conduit routing, and have a working mental model of how fiber moves through a data center building
- • Working knowledge of data center network architecture at scale: CLOS topologies, spine-leaf fabric design, ToR switching, and how production ML fabrics differ from standard compute deployments
- • Experience coordinating across construction, facilities, and network engineering in a live build environment, including managing the interface between construction milestones and network deployment readiness
- • Demonstrated ability to manage multiple concurrent workstreams across organizational boundaries without losing precision on any of them
- • Comfort going deep on technical dependencies while communicating clearly at the leadership level: you can translate a fiber tray congestion issue into a program risk statement for Director level audience without losing accuracy

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