Machine Learning Engineer

Job not on LinkedIn

🕒 August 20

🏄 California, Illinois, +1 more states – Remote

🏄 California – Remote 🌽 Illinois – Remote 🤠 Texas – Remote

💵 $170k - $250k / year

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 Machine Learning Engineer

👻 Ghost score 39%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Guardian Industries - DeWitt

WebsiteLinkedInAll Job Openings

- employees

🏭 Manufacturing

🏗️ Construction

Manufacturing • Construction

Guardian Industries - DeWitt is a facility or operating unit of Guardian Industries listed among Koch companies. Guardian Industries is a global manufacturer best known for producing glass and building-related products and operates multiple manufacturing sites; the DeWitt location is presented in Koch’s careers/company listings as one of the Guardian Industries locations offering roles in manufacturing, engineering, operations and related functions. As part of Koch’s portfolio, the site is positioned within a large industrial/manufacturing network and supports production, operations and career opportunities tied to Guardian’s glass and building products businesses.

📋 Description

• Build physics-informed surrogate models on Azure Machine Learning to predict engineering simulation outcomes from design parameters • Design and train surrogate models, including neural networks, Gaussian processes, gradient-boosted trees, GNNs, and PINNs, on Azure GPU compute • Incorporate physics-informed constraints to keep predictions physically valid • Build model-uncertainty and confidence scoring to identify designs requiring full simulation validation • Retrain models as new simulation results arrive • Deploy and version models through Azure ML endpoints and model registry • Monitor models for drift on a rolling basis • Benchmark surrogate versus full-simulation speedup to guide platform-level performance tuning • Partner with data scientists, LLM engineers, and MLOps teams to maintain reliable and fast GPU-heavy training and simulation workloads

🎯 Requirements

• 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, including surrogate modeling, physics-informed ML, or scientific ML • Strong Python with PyTorch or TensorFlow • Understanding of relevant engineering/physics fundamentals and simulation data formats • Experience with Azure Machine Learning or a similar cloud ML platform • Familiarity with uncertainty quantification, including Bayesian approaches and ensembling • Direct experience with industry-standard EM or physics simulation tools is advantageous • Geometric deep learning, including graph neural networks and mesh-based models, for CAD data is advantageous • Background in RF/high-speed electronics or interconnect design is advantageous

🏖️ Benefits

• Medical insurance • Dental insurance • Vision insurance • Flexible spending accounts • Health savings accounts • Life insurance • Accidental death and dismemberment (ADD) insurance • Disability insurance • Retirement benefits • Paid vacation/time off • Educational assistance • Infertility assistance • Paid parental leave • Adoption assistance • Flexible work environment