Digital twins — AI-powered virtual replicas of physical systems — are a $110B market by 2028.

And almost nobody is talking about the career opportunity.

What digital twins do: simulate a factory before building it. Test autonomous vehicle scenarios without real cars. Model a city's traffic patterns to optimize signal timing. Predict equipment failures by simulating wear over time.

NVIDIA Omniverse. Microsoft Azure Digital Twins. Siemens Xcelerator. GE's Predix. All building digital twin platforms.

The engineering roles: Simulation Engineer (building accurate virtual replicas). Physics-Informed ML Engineer (models that respect physical laws). Real-Time Visualization Engineer (rendering twins for human interaction). IoT + ML Integration Engineer (connecting physical sensors to virtual models).

What makes this field special: it requires ML engineers who also understand physics, mechanical engineering, or domain-specific science. The models aren't just pattern matching — they need to respect the laws of thermodynamics, fluid dynamics, structural mechanics.

This interdisciplinary requirement means the talent pool is small and the salaries are excellent: $160K-$300K for experienced engineers.

Industries adopting digital twins fastest: manufacturing, aerospace, automotive, energy, urban planning, and healthcare (digital twins of individual patients for treatment planning).

If you have engineering or science background PLUS ML skills, digital twins might be the most natural and lucrative career intersection you'll find.