Description
Molex is hiring a Machine Learning Engineer to build physics-informed surrogate models on Azure Machine Learning that predict engineering simulation outcomes from design parameters. The role involves training neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs on Azure GPU compute, incorporating physical constraints, quantifying model uncertainty, deploying and monitoring models, and benchmarking surrogate-model speedup against full simulations. Candidates need extensive production ML experience, 10+ years building ML for physical or engineering systems, strong Python and PyTorch or TensorFlow skills, engineering or physics fundamentals, and Azure Machine Learning experience. The role is available in Austin, Texas; Fremont, California; and Lisle, Illinois, with remote eligibility, and pays $170,000–$250,000 per year plus variable pay.
