Machine Learning Engineer

Molex is hiring a Machine Learning Engineer for an Austin, TX onsite role. This position focuses on building physics-informed surrogate models on Azure Machine Learning to predict engineering simulation outcomes from design parameters. The result is faster iteration and reduced reliance on full high-fidelity simulation, with models deployed, monitored, and improved over time.

Compensation: USD 170,000 - 250,000 per yearly

Experience: 10+ years

What you will be doing

• Design and train surrogate models using approaches such as neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs on Azure GPU compute (ND/NC series).
• Incorporate physics-informed constraints so predictions remain physically valid rather than purely statistical fits.
• Create uncertainty and confidence scoring to determine which designs require full simulation validation, then retrain as new results arrive.
• Deploy and version models through Azure ML endpoints and the model registry, with ongoing rolling drift monitoring.
• Benchmark surrogate performance against full simulation to quantify speedups and support platform-level performance tuning.

What you will need

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

Benefits

• Medical, dental, and vision coverage
• Flexible spending and health savings accounts
• Life insurance
• AD&D (ADD) and disability coverage
• Retirement benefits
• Paid vacation/time off
• Educational assistance
• May include infertility assistance
• Paid parental leave and adoption assistance

What 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