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AI Cloud Data Egress Cost Optimization Engineer

David Dubois

Full-time · Job · Los_Angeles

About the role

You'll be the fourth infrastructure engineer on a team of 14, working alongside platform, ML, and SRE. We already run large-scale model training and inference across multiple cloud regions, and our data egress bill has quietly become one of our largest line items as datasets move between regions and providers. What's missing is someone who can model exactly where that traffic comes from and design routing and caching strategies that cut cost without touching performance. You'll work with defined sprints, clear ownership, and weekly syncs with the infrastructure lead so priorities never get lost between teams.

Responsibilities

  • Model data egress cost drivers across cloud regions and providers
  • Design caching and routing strategies to reduce cross-region transfer
  • Partner with ML teams to identify avoidable data movement
  • Report cost savings and recommendations to infrastructure leadership

Requirements

  • 5+ years in infrastructure or cloud cost engineering roles
  • Deep hands-on experience with Kubernetes and multi-cloud networking
  • Familiarity with MLflow or similar experiment tracking tools

Benefits

  • Fully remote with quarterly team offsites
  • Competitive salary with annual refresh grants
  • Dedicated budget for GPU experimentation sandboxes

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Categories

MLOps & AI Infrastructure

Skills

kubernetes docker aws mlflow airflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

kubernetes docker aws mlflow airflow

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