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Sr ML/AI Engineer

PamTen Inc

  • 30+ days ago
  • Remote

    Highlights

    Working within a Google Cloud Vertex AI environment, you will leverage sensor data (vibration, audio, location) captured by Pixel phone hardware kits on revenue cars and ground-truth defect records from MTA's Hexagon system to build predictive models supporting the TrackInspect application. Description: Client is seeking an ML/AI Engineer to develop, fine-tune, and operationalize machine learning models that predict track nonconformities on the NYC subway system.

    Numbers & Facts

    Location (
    Remote    )

    Description

    Description: Client is seeking an ML/AI Engineer to develop, fine-tune, and operationalize machine learning models that predict track nonconformities on the NYC subway system. Working within a Google Cloud Vertex AI environment, you will leverage sensor data (vibration, audio, location) captured by Pixel phone hardware kits on revenue cars and ground-truth defect records from MTA's Hexagon system to build predictive models supporting the TrackInspect application.

    Key Responsibilities
    • Perform feature engineering on sensor and operational data to identify patterns correlated with documented track defects in Hexagon (HxGN)
    • Leverage and refactor existing prototype ML models for pilot-scale use; validate performance and suitability for production
    • Utilize Vertex AI Workbench to train, retrain, and version ML models; track experiments and metrics
    • Conduct hyperparameter tuning and model validation activities to optimize nonconformity prediction accuracy
    • Deploy trained models for daily batch inference; connect deployed models to batched sensor input pipelines
    • Implement model monitoring, alerting, and a continuous feedback loop from Track Inspector inputs captured in HxGN
    • Build and maintain CI/CD pipelines and multi-environment deployment strategy for current models
    • Build the BigQuery 'Predictions – User Friendly View' incorporating location attributes, prediction metadata, and feedback fields
    • Assess model results against known track defects within HxGN and present findings at model evaluation meetings with MTA
    • Proficiency in Python (scikit-learn, TensorFlow or PyTorch) and SQL
    • Hands-on Vertex AI experience (Workbench, training pipelines, model deployment)
    • Strong understanding of feature engineering, model validation, and hyperparameter optimization
    • Experience with time-series or sensor data (vibration, audio, or accelerometer signals a plus)
    • Familiarity with BigQuery as a data platform for ML pipelines
    • Experience with geospatial ML or GPS/LRS-based positioning models
    • Prior work in predictive maintenance, anomaly detection, or IoT sensor analytics
    • Google Professional ML Engineer certification
    • Understanding of transit or infrastructure asset management systems
    • Experience with Looker Studio or similar BI tools for ML output visualization

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