Description
The role is a hands-on ML engineering position focused on owning product-development ML systems, including CNN/U-Net/ViT models for inspection and measurement, real-time anomaly detection, surrogate modeling, active learning, data-to-model interfaces, MLOps, and model evaluation. The engineer will also mentor junior team members and document model decisions for production handoff. The posting requires a bachelor’s or master’s degree in an AI, machine learning, computer science, or related field, 1–3 years of hands-on ML engineering experience, and strong Python, PyTorch, computer vision, surrogate modeling, active learning, and MLOps skills.
