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AI Model Evaluation Reproducibility Engineer

Aria Watanabe

Full-time · Job · Los_Angeles

About the role

Have you ever tried to reproduce a model evaluation result from six months ago and given up because the eval harness, dataset version, and prompt template had all quietly drifted? That's the exact problem we're hiring you to solve. We publish internally and encourage conference submissions, so our evaluation pipelines need to survive scrutiny from both internal red teams and external reviewers. You will build versioning and reproducibility tooling around our evaluation stack, covering dataset snapshots, prompt templates, and model checkpoints, so any result can be regenerated exactly a year later. This work matters because our safety claims are only as strong as our ability to reproduce them, and right now that ability is weaker than it should be for a lab our size.

Responsibilities

  • Build reproducibility tooling for the evaluation pipeline
  • Version datasets, prompt templates, and model checkpoints together
  • Audit past evaluation runs for reproducibility gaps
  • Partner with researchers to define reproducibility standards
  • Present findings at internal research reviews

Requirements

  • 5+ years in ML infrastructure or research engineering
  • Experience with MLflow or similar experiment tracking tools
  • Strong understanding of evaluation methodology
  • Comfortable working closely with research scientists

Benefits

  • Fully remote
  • Research compute budget
  • Health insurance
  • Conference and publication support

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Categories

AI & Machine Learning Engineers

Skills

python pytorch mlflow statistics

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Language

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Salary Range

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AI Expertise

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Skills

python pytorch mlflow statistics

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