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AI Experiment Compute Cost Attribution Developer

Connor Leclerc

Contract-to-hire · Job · Chicago

About the role

We serve ML teams at companies running hundreds of experiments in parallel, and right now the only cost visibility they get is a single lump AWS bill at the end of the month. Our model registry and feature store already track model and feature lineage well, but nobody can tell a team lead which experiment, which researcher, or which dataset actually drove last month's GPU spend. We're looking for a senior engineer to build cost attribution that ties every dollar of compute back to a specific experiment run, so teams can finally see where their budget goes. This starts as a contract engagement with a clear path to a full-time offer for the right person.

Responsibilities

  • Build cost attribution linking AWS compute spend to individual experiment runs
  • Integrate attribution data into the existing model registry and feature store
  • Work with MLflow to tag experiment metadata needed for cost tracing
  • Investigate historical spend to validate attribution accuracy
  • Propose budget alerts based on per-team or per-project spend trends

Requirements

  • 5+ years of experience with ML infrastructure or cloud cost engineering
  • Experience with MLflow or similar experiment tracking tools
  • Strong SQL and data pipeline skills
  • Familiarity with AWS billing and cost allocation tooling
  • Comfortable working independently on a contract basis initially

Benefits

  • Hybrid role, flexible office attendance in Austin
  • Competitive contract rate with path to full-time
  • Health insurance available after conversion
  • Direct collaboration with senior ML infrastructure engineers
  • Opportunity to shape long-term cost strategy

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Categories

MLOps & AI Infrastructure

Skills

python mlflow docker aws sql

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Job Type

Contract-to-hire

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python mlflow docker aws sql

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