Physics-Informed ML Engineer: Azure Surrogates

Molex; Lisle (IL); On-site; USD 170,000 - 250,000; Full time

Benefits offered by this job

Medical

Dental

Vision

401(k) match

Paid time off

Job summary

Molex is seeking an ML Engineer to build physics-informed surrogate models on Azure ML that predict engineering outcomes from design parameters. You will pre-screen designs in milliseconds to accelerate the design-optimization cycle.

We expect 10+ years building ML for physical systems, strong Python with PyTorch or TensorFlow, and experience with Azure ML. You will collaborate with data scientists, LLM engineers, and MLOps to keep GPU-heavy workloads fast and reliable.

Qualifications

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

Responsibilities

• Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series).
• Incorporate physics-informed constraints so predictions stay physically valid, not just statistically fit.
• Build model-uncertainty and confidence scoring to decide which designs need full simulation validation, then retrain as new results arrive.
• Deploy and version models via Azure ML endpoints and model registry; monitor for drift on a rolling basis.
• Benchmark surrogate vs. full-simulation speedup to guide platform-level performance tuning.

Skills

Surrogate modeling

Physics-informed ML

Deep learning

Python

Azure ML

Uncertainty quantification

GNNs/PINNs

Education

PhD or MS in ML, CS, or engineering

Tools

Azure Machine Learning

Job description

Molex is seeking an ML Engineer to build physics-informed surrogate models on Azure ML that predict engineering outcomes from design parameters. You will pre-screen designs in milliseconds to accelerate the design-optimization cycle.

We expect 10+ years building ML for physical systems, strong Python with PyTorch or TensorFlow, and experience with Azure ML. You will collaborate with data scientists, LLM engineers, and MLOps to keep GPU-heavy workloads fast and reliable.