Machine Learning Engineer

• Employee Login
• Home
• Search Jobs
• Life at Koch
• Companies
• Career Opportunities
• How We Hire
• Early Careers
• Recruiting Events
• Candidate Portal
• Join our Talent Community
• Employee Login
• Learn More About Koch
• Latest Koch News
• English - US
• Deutsch
• English - US
• Español - ES
• Français - France
• Polski, 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., * Strictly Necessary Cookies

Strictly Necessary Cookies

Always Active

Strictly Necessary Cookies

These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. These cookies do not store any personally identifiable information.

Cookie Details

• Performance Cookies

Performance Cookies

Performance Cookies

These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.

Cookie Details

• Functional Cookies

Functional Cookies

Functional Cookies

These cookies enable the website to provide enhanced functionality and personalisation. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly.

Cookie Details

• Targeting Cookies

Machine Learning, English Language, Facebook, LinkedIn, Physics, Simulation, Microsoft Windows Azure, Machine Learning, Candidate Screening, Data Science, * 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 and detail of our compensation philosophy.

Hiring Philosophy

All Koch companies value diversity of thought, perspectives, aptitudes, experiences, and backgrounds. We are Military Ready and Second Chance employers. Learn more about our hiring philosophy here.

As a Koch company, Molex is a leading supplier of connectors and interconnect components, driving innovation in electronics and supporting industries from automotive to health care and consumer to data communications. The thousands of innovators who work for Molex have made us a global electronics leader. Our experienced people, groundbreaking products and leading-edge technologies help us deliver a wider array of solutions to more markets than ever before.

At Koch, employees are empowered to do what they do best to make life better. Learn how our business philosophy helps employees unleash their potential while creating value for themselves and the company.