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# Sr ML/AI Engineer

- **Company:** PamTen
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Bug Tracking Systems, Software Bug Management, Cloud Computing, Data Infrastructure, Global Positioning Systems (GPS), Python (Programming Language), Machine Learning, Operational Data Store, Tensorflow, Standard Sql, Server Administration, Google Cloud, Feature Engineering, Pytorch, Google Data Studio, Model Validation, Scikit Learn, Machine Learning Operations
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/sr-ml-ai-engineer--6b1d3dd8-4488-48af-b362-1ca5cc475053

## About the Role

Artificial Intelligence (AI), Predictive Modeling, Fast Food, Artificial Intelligence (AI), Machine Learning, Cloud Computing, MTA, Software Administration, Bug Tracking/Defect Management, Prototyping, * 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

## 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., * 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)