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AI Data Versioning Infrastructure Engineer

Gravex Cloud

Remote · Under $60k / year · Full-time · Posted 5 months ago

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About the role

"Which version of the training data produced this model?" It's a simple question that most ML teams can't answer. Our data versioning platform tracks every change to every dataset — additions, deletions, schema changes, quality fixes — and links data versions to the models trained on them. When a model misbehaves, teams can trace back to the exact data it saw. As a junior engineer, you'll work on the core versioning infrastructure: the diff engine that efficiently computes changes between dataset versions, the storage layer that deduplicates unchanged data, and the API that data scientists use to check out specific versions for training. Think of it as Git for datasets, but optimized for terabyte-scale tabular and image data. The data infrastructure team is four engineers. You'll pair with the senior engineer who designed the storage layer, starting with the API endpoints that serve version metadata. The work requires careful thinking about data structures and storage efficiency — a poorly designed diff format can make versioning cost more than just keeping full copies.

Fully remote.

Responsibilities

  • Build API endpoints for dataset version management and checkout
  • Implement diff computation between dataset versions
  • Optimize storage through deduplication of unchanged data
  • Write integration tests for versioning operations
  • Monitor storage costs and identify optimization opportunities
  • Maintain documentation for the versioning API and data model

Requirements

  • 1+ year of Python development with database experience
  • Understanding of data structures and storage efficiency concepts
  • Familiarity with REST API design and Docker
  • Interest in data infrastructure and MLOps
  • Ability to write clean, well-tested code

Benefits

  • Fully remote across EU time zones
  • 28 days PTO
  • €1,500 learning budget
  • Mentorship from senior data infrastructure engineers
  • Work on a foundational MLOps problem

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Categories

MLOps & AI Infrastructure Data Science & Analytics

Skills

python sql docker aws mlflow

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Job Type

Full-time

Level

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Language

English

Salary Range

Under $60k / year

AI Expertise

MLOps & AI Infrastructure Data Science & Analytics

Skills

python sql docker aws mlflow fastapi

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