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Platform Engineer - ML Training Pipelines

Elena Popescu

Full-time · Mid-level · Berlin

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About the role

Our research and applied ML teams move quickly, but the training stack around them is inconsistent and expensive. We need a platform engineer to standardise how experiments run, how artefacts are tracked, and how data workflows move into production. The role is less about inventing a platform from scratch and more about reducing chaos without slowing people down.

Responsibilities

  • Improve training pipeline reliability and reproducibility
  • Create templates and standards for model jobs and artefact tracking
  • Reduce cloud waste through better scheduling and environment controls
  • Partner with ML teams on smoother experiment-to-production handoffs
  • Support observability for long-running jobs and flaky workflows

Requirements

  • 4+ years in platform engineering, data infrastructure, or MLOps
  • Python for internal tooling and automation
  • Kubernetes and Docker for repeatable workloads
  • Airflow for scheduled workflows and dependency management
  • GCP or AWS experience in production teams
  • CI/CD experience for infrastructure and service releases

Benefits

  • $130,000 - $152,000 base salary
  • Hybrid in Berlin or fully remote in Germany
  • Learning budget and home office support
  • Transparent engineering leadership
  • A role with both platform depth and product relevance

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes airflow gcp ci/cd

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Job Type

Full-time

Level

Mid-level

Language

English

Salary Range

$130,000 - $152,000

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes airflow gcp ci/cd

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