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Machine Learning Engineer

Molex Lisle, IL

Lisle, IL

Today

$170k - $250k/yr Full time Python Artificial Intelligence Azure Gpu Compute

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Job Description

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.

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