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AI Cost-Aware Autoscaler Developer

Mia Levy

Full-time · Job · Los_Angeles

About the role

The autoscaling decisions you'll make will directly control GPU spend for over 40 enterprise clients running models on our deployment platform. Right now our scaling logic reacts to load but ignores cost, which means clients occasionally pay for headroom they don't need. We're looking for an engineer to build a cost-aware autoscaler that balances latency requirements against GPU spend in real time. Our clients include regulated enterprises that expect predictable, auditable infrastructure behavior, so reliability and cost transparency both matter here.

Responsibilities

  • Build autoscaling logic that factors in real-time GPU cost data
  • Integrate with existing Kubernetes-based deployment infrastructure
  • Define and track SLOs for latency versus cost tradeoffs
  • Work with MLflow to track scaling experiment outcomes
  • Document scaling decisions for enterprise client audits

Requirements

  • 4+ years of experience with Kubernetes and container orchestration
  • Experience building or operating autoscaling systems
  • Familiarity with AWS GPU instance pricing models
  • Comfortable working with MLflow or similar experiment tracking tools
  • Ability to write clear documentation for enterprise clients

Benefits

  • Fully remote position
  • Competitive salary plus equity
  • Health, dental, and vision insurance
  • Annual infrastructure conference budget
  • Flexible PTO policy

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws mlflow

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