Machine Learning Engineer

Molex is building an engineering optimization workflow that replaces slow simulation loops with physics-aware machine learning. In this role, you will develop physics-informed surrogate models on Azure Machine Learning, turning design parameters into simulation-ready predictions in milliseconds so teams can pre-screen candidate designs faster. You will also deploy, version, and continuously evaluate these models in production alongside data science and MLOps partners.

Location: Lisle, IL 60532 (onsite)
Compensation: USD 170,000 - 250,000 per yearly
Experience: 10+ years

What You’ll Do

• Design and train surrogate models using neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs.
• Run model training on Azure GPU compute (ND/NC series).
• Incorporate physics-informed constraints so predictions remain physically valid rather than only statistically well-fit.
• Create model-uncertainty and confidence scoring to identify which designs require full simulation validation, then use new validation results to retrain.
• Deploy and manage model lifecycle using Azure ML endpoints and model registry, including rolling drift monitoring.
• Benchmark surrogate performance against full-simulation speedup to inform platform-level performance tuning.

Requirements

• Extensive hands-on experience building, training, and deploying ML models in production, not only consuming pretrained APIs.
• 10+ years building ML for physical or engineering systems, including surrogate modeling, physics-informed ML, or scientific ML.
• Strong Python experience with PyTorch or TensorFlow.
• 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.

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
• GPU-heavy training

Benefits

• Medical
• Dental
• 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
• Adoption assistance

Who Will Put You Ahead

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