Simulation & Modeling Data Jobs

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Machine Learning Engineer

Molex Austin, TX

Austin, TX

Today

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

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Job Description

Molex is hiring a Machine Learning Engineer in Austin, TX (onsite) to help reduce reliance on full high-fidelity engineering simulations. This role focuses on building and operationalizing physics-informed surrogate models on Azure Machine Learning, so design teams can predict simulation outcomes from design parameters faster, with uncertainty-aware decisions about when full validation is still needed. The salary range is USD 170,000 - 250,000 per year.

What you’ll build and improve

Design and train surrogate models using neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs on Azure GPU compute (ND/NC series).
Apply physics-informed constraints so model outputs remain physically valid, not just statistically accurate.
Create model uncertainty and confidence scoring to identify which designs require full simulation validation.
Continuously retrain models as new simulation results arrive, improving performance over time.

Deploy, monitor, and benchmark

Deploy and version models using Azure ML endpoints and the model registry.
Monitor models on a rolling basis for drift.
Benchmark surrogate performance against full simulation to support platform-level performance tuning.

What you bring

Extensive hands-on production experience building, training, and deploying ML models (not limited to pretrained API usage).
10+ years building ML for physical or engineering systems, including surrogate modeling, physics-informed ML, or scientific ML.
Strong Python skills 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, including Bayesian approaches and ensembling.

Additional advantages

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

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

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