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AI Predictive Maintenance Engineer

Ethan Park

Full-time · Job · Berlin

About the role

Industrial equipment fails in patterns — but only if you know how to read the sensor data. Our predictive maintenance platform monitors 15,000 machines across manufacturing plants in Germany and Austria, using ML to predict failures 2-4 weeks before they happen. That advance warning lets maintenance teams schedule repairs during planned downtime instead of dealing with emergency breakdowns. You'll build and maintain the predictive models: anomaly detection on sensor time series, remaining useful life estimation, and failure mode classification. The data is messy — sensors drift, calibrations change, and machines operate differently depending on production schedules. Your models need to handle all of this robustly. We work closely with domain engineers who understand the machines. You bring the ML expertise. Together, you'll build models that maintenance technicians actually trust — which means interpretability matters as much as accuracy.

Responsibilities

  • Build anomaly detection models for industrial sensor time series data
  • Develop remaining useful life estimation models for critical equipment
  • Design failure mode classification systems with interpretable outputs
  • Handle sensor drift and calibration changes in production models
  • Collaborate with domain engineers on feature engineering and model validation

Requirements

  • 3+ years in ML with time series or sensor data experience
  • Strong Python skills with PyTorch and scikit-learn
  • Experience with anomaly detection techniques
  • Understanding of signal processing fundamentals
  • Interest in industrial applications and predictive maintenance

Benefits

  • Fully remote across European time zones
  • 30 days PTO
  • Home office equipment stipend
  • Impact on industrial sustainability and efficiency

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn sql pandas

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Level

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Language

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Salary Range

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