Applied AI/ML Engineer

• Design and maintain the preprocessing pipeline from raw sensor data to ML-ready representations
• Develop, train, and evaluate fault detection and classification models
• Own algorithms for event localization and improve accuracy under real-world conditions
• Package models for constrained edge hardware and/or cloud inference, and define monitoring strategy for production deployments

This role directly contributes to making the electrical grid more reliable and efficient by turning edge data into operational insights, reducing outages and improving energy distribution.

• Production experience training and evaluating classification or anomaly detection models on time-series or spectral data
• Solid grounding in model evaluation: precision/recall tradeoffs, confusion matrices, calibration, handling class imbalance
• Experience with data pipeline tooling and feature engineering from raw sensor data
• Python proficiency: NumPy, SciPy, PyTorch or TensorFlow, scikit-learn or similar

Compensation and benefits not specified in the job posting.

GridIQ turns edge data from physical systems into actionable grid intelligence, focusing on improving the reliability and efficiency of the electrical grid.