Machine Learning Engineer

Molex is looking for a Machine Learning Engineer to build physics-informed surrogate models that help engineering teams move faster. This role focuses on using Azure Machine Learning and Azure GPU compute (ND/NC series) to predict simulation outcomes from design parameters, reducing the need to run every full high-fidelity simulation. You will deploy, monitor, and continuously improve models using uncertainty scoring and retraining as new validation results come in.

What you’ll do

• Design and train surrogate models such as neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs using Azure GPU compute.
• Apply physics-informed constraints so predictions remain physically valid, not just statistically accurate.
• Implement model uncertainty and confidence scoring to determine which designs require full simulation validation, then feed results back into the training cycle.
• Deploy and version models through Azure ML endpoints and model registry, with rolling drift monitoring.
• Benchmark surrogate model performance against full simulation to quantify speedup and support platform-level optimization.

What you bring

• Extensive hands-on production ML experience, including building, training, and deploying models (not just integrating pretrained APIs).
• 10+ years building ML for physical and engineering systems, including surrogate modeling, physics-informed ML, or scientific ML.
• Strong Python skills with PyTorch or TensorFlow.
• Working understanding of relevant engineering/physics fundamentals and the simulation data formats for your domain.
• Experience with Azure Machine Learning or a similar cloud ML platform.
• Familiarity with uncertainty quantification, including Bayesian approaches and ensembling.

Tools and technologies

Azure Machine Learning, Azure GPU compute (ND/NC series), Python, PyTorch, TensorFlow, Azure ML endpoints, model registry, neural networks, Gaussian processes, gradient-boosted trees, GNNs, PINNs, Bayesian approaches, ensembling.

Compensation and location

Austin, TX (onsite) · USD 170,000 - 250,000 per year · 10+ years minimum experience.

Benefits

• Medical, dental, and vision
• Flexible spending and health savings accounts
• Life insurance
• ADD
• Disability
• Retirement
• Paid vacation/time off
• Educational assistance
• May also include infertility assistance
• Paid parental leave and adoption assistance

What will put you ahead

• Direct experience with industry-standard EM or physics simulation tools
• Geometric deep learning for CAD data, including graph neural networks and mesh-based models
• Background in RF/high-speed electronics or interconnect design