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AI Experiment Reproducibility Platform Developer

Connor Leclerc

Full-time · Job · Chicago

About the role

A data scientist runs an experiment in January, gets a great result, and moves on to the next project. Six months later, a colleague tries to reproduce it and cannot. The model was trained on a dataset that has been updated. The Docker image used a library pinned to a specific version that has since been overwritten. The random seed was not logged. The infrastructure that would have reproduced the run no longer exists in that configuration. Reproducibility is not a nice-to-have for regulated industries — it is a compliance requirement. And even outside regulated industries, it is the foundation of scientific ML practice. We build the reproducibility layer for enterprise ML platforms: the tooling that captures everything needed to reproduce an experiment at a later date, and the automation that validates reproduction actually works. You'll extend our existing experiment tracking integration to add full environment capture, dataset versioning, and automated reproduction tests that run on a schedule and alert teams when historical experiments can no longer be reproduced.

Responsibilities

  • Build full environment capture for Docker, library dependencies, and random state
  • Implement dataset version linking to prevent silent data drift in reproduced experiments
  • Design automated reproduction tests that periodically verify historical experiment validity
  • Create alerts when reproduction failures indicate infrastructure or data changes
  • Integrate reproducibility tooling with MLflow, Weights & Biases, and custom trackers
  • Build reproducibility audit reports for compliance team submission

Requirements

  • 3+ years in MLOps or data engineering with ML experiment management experience
  • Strong Python and Docker skills
  • Experience with experiment tracking systems and ML pipeline orchestration
  • Understanding of software reproducibility concepts: determinism, environment capture, versioning
  • Familiarity with regulated industry requirements for ML audit trails

Benefits

  • Fully remote
  • Health and dental
  • $3,000 annual learning budget
  • Equity participation
  • Quarterly team meetups

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes 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 Automation & AI Workflows

Skills

python docker kubernetes aws mlflow airflow

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