Machine Learning Engineer

Your Job

The ML Engineer will build physics-informed surrogate models on Azure Machine Learning that predict engineering simulation outcomes directly from design parameters.

They will be pre-screening candidate designs in milliseconds so only the most promising ones require full high-fidelity simulation, accelerating the design-optimization cycle.

Our Team

Established in 1938, Molex delivers comprehensive electronic solutions for various markets, including data communications, telecommunications, consumer electronics, industrial, automotive, commercial vehicle, aerospace and defense, medical, and lighting.

You'll join the platform team behind our Azure AI/ML engineering tools, partnering closely with data scientists, LLM engineers, and MLOps teams to keep GPU-heavy training and simulation workloads reliable and fast.

What You Will Do


* 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.

Who You Are (Basic 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).

What Will Put You Ahead


* Direct experience with industry-standard EM or physics simulation tools.


* Geometric deep learning (graph neural networks, mesh-based models) for CAD data.


* Background in RF/high-speed electronics or interconnect design.

For this role, we anticipate paying $170,000 - $250,000 per year.

This role is eligible for variable pay, issued as a monetary bonus or in another form.

At Koch companies, we are entrepreneurs.

This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions.

Any compensation range provided for a role is an estimate determined by available market data.

The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location.

If you have questions, please speak to your recruiter about the flexibility a...