Project Engineer, Machine Learning​/ ML Engineer

Design and deploy Deep Learning models for automated paint inspection, enhancing defect detection accuracy in a production environment. Streamline real-time inference pipelines to minimize latency when models are deployed at plant by ensuring optimal use of hardware resources like GPUs. Integrate AI models into current system, streamline the end-to-end workflow from data ingestion to model inference. Reduce infrastructure costs and deployment time by refactoring model architectures and optimize hardware utilization for multi-plant scalability.

Label and preprocess massive datasets to support robust Deep Learning training and testing pipelines.

Basic Qualifications:

• Experience designing and deploying Deep Learning models
• Experience with automated defect detection or computer vision systems
• Experience optimizing real-time inference pipelines
• Experience working with GPU hardware for model deployment
• Experience integrating AI models into existing systems
• Experience building end-to-end ML workflows
• Experience optimizing model architectures for performance and scalability
• Experience handling and preprocessing large-scale datasets
• Experience supporting training and testing pipelines

Preferred Qualifications:

• Master’s degree in Electrical and/or Computer Engineering or Electronic Engineering with 6 months experience as Software or AI Engineer or related.