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AI Model Lineage Tracking Developer

Connor Leclerc

Full-time · Job · Chicago

About the role

Three months ago, a model in production was making predictions that degraded silently for 11 days before anyone noticed. The postmortem showed the root cause: a training dataset had been updated without triggering a model retraining, and the lineage graph connecting dataset versions to model versions simply didn't exist. We're building it now. Model lineage is deceptively complex: a production model might depend on five upstream datasets, twelve feature transformations, eight hyperparameter decisions, and a specific version of three library dependencies — all of which can change independently. You'll design and build the tracking system that captures these dependencies automatically as models move through training, evaluation, and deployment, and surfaces the graph in a form that on-call engineers can query during incidents. The platform team is five engineers. You'll own the lineage subsystem: data model design, ingestion APIs, query interface, and the UI hooks that display lineage in our model registry. The first milestone is: given any model version currently in production, answer 'what changed since the last good version' in under 10 seconds.

Responsibilities

  • Design the data model for capturing model-to-dataset-to-feature lineage relationships
  • Build ingestion APIs that capture lineage automatically during training and deployment
  • Implement graph query interface for lineage traversal and diff computation
  • Create UI hooks exposing lineage information in the model registry
  • Build lineage-aware alerting that flags when upstream datasets change without model update
  • Write migration path for backfilling lineage data from existing training runs

Requirements

  • 3+ years in ML platform or data engineering
  • Experience with metadata management, data lineage, or knowledge graphs
  • Strong Python and SQL skills with graph data structure understanding
  • Familiarity with MLflow, Weights & Biases, or similar experiment tracking tools
  • Ability to design APIs that are easy to instrument without disrupting existing workflows

Benefits

  • Fully remote with async-first culture
  • Health, dental, and vision insurance
  • 401(k) with match
  • $3,000 annual learning budget
  • Home office stipend

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Categories

MLOps & AI Infrastructure data science & analytics

Skills

python docker airflow aws sql

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Salary Range

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AI Expertise

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Skills

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