Principal Engineer, Machine Learning

Most ML engineering roles at large companies mean contributing to a platform team where your work disappears into a pipeline that fifty other engineers also touch. This role is different. You will be the primary owner of the ML systems that detect product development defects, model material behavior with limited data, and select the highest-value experiments from an active learning pipeline.

Your models will run in product development. Your decisions will matter immediately. Key

Deep Learning Model Implementation & Product Development Ownership: Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement. Own model performance end-to-end — ablation studies, confidence calibration, product development performance monitoring. Anomaly Detection Systems: Build and maintain real-time anomaly detection for product development sensor and time-series data streams — statistical baseline, threshold calibration, drift alerting.

Sole implementation owner for this workstream. Surrogate Modeling & Active Learning Operations: Own implementation and iteration of surrogate model pipelines and active learning systems under Technical lead’s architectural direction. Configure acquisition functions; integrate with versioned feature sets.

Data-to-Model Interface Ownership: Own the data contract between the Data Engineer and the ML model stack. Define feature specifications, validate datasets against model input requirements, and escalate data