Hiring nowSimulation, digital twins and physical AI assurance

Digital Twin Engineer

Also called Industrial Digital Twin Engineer

A Digital Twin Engineer keeps a virtual model of a physical asset or process accurate and useful. The company gets predictions it can trust for operating, maintenance and design decisions without testing everything on the real system.

What they do

The work starts with a physical asset or process and a question someone needs answered. The engineer builds or adapts a model, then calibrates it against sensor data and field observations. They identify parameters that cannot be measured directly. They design the workflow that keeps the model's state in step with the real system as it changes, wears and drifts. They check where predictions fail and why. They work with controls, operations and software teams to decide what the twin should represent and what it can leave out. They document assumptions so others know when to trust the output.

What a strong hire delivers

In the first months, a strong hire delivers a calibrated twin of one real asset or process, with a clear statement of where it is accurate and where it is not. They set up an update workflow that brings in new field data and retunes the model without manual rework. They also agree with the business which decisions the twin will support, and how its usefulness will be judged.

How you can tell they are good

Ask them to walk through a twin they kept running against a real system. Good candidates explain how they identified parameters, handled noisy or missing data, and detected drift. They can show a case where the model was wrong and what they changed. They talk about decisions the twin informed, not only model detail. Be wary of anyone who only describes building the model.

Why it is hard to fill

Titles vary widely and the same work is often called simulation, controls or systems engineering. Many candidates have built simulations but never kept one aligned with a live physical system. That second skill, system identification and state synchronisation, is the scarce part. Strong people are often settled in industrial or robotics teams, so we search by what they have done, not by job title.

Where to find them

Look to simulation engineers, control engineers, systems engineers and verification engineers. They come from robotics companies, industrial automation firms, energy and process operators, aerospace and automotive teams, and makers of simulation software. Some move across from reliability or condition monitoring work. Those who have run models against field data tend to adapt fastest to AI companies.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Digital Twin Engineer

  1. Tell me about a twin or model you kept aligned with a real system. What did you have to keep updating?

    Strong answers name the asset, the data sources and the parameters that drifted. They describe a repeatable update process, not occasional manual retuning. Worry if the story ends once the model was first built.

  2. How do you identify parameters you cannot measure directly?

    Look for excitation design, identifiability checks, estimation methods and uncertainty bounds. Good candidates mention validating on held-out data. Worry about answers that rely on fitting until the curves look right.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Digital Twin Engineer to build and maintain operational models of our physical assets or processes. You will keep each twin calibrated against real data so that our teams can rely on its predictions when making decisions.

What you will do

  • Build and maintain calibrated models of physical assets or processes
  • Identify model parameters from sensor data and field observations
  • Design workflows that keep twin state synchronised with the real system
  • Measure prediction fidelity and report where the model is and is not reliable
  • Investigate divergence between predictions and field results
  • Work with controls, software and operations teams to define what each twin must support
  • Document assumptions, limits and update procedures

What you will bring

  • Demonstrated work keeping a simulation or model aligned with a real physical system
  • Hands-on experience with system identification and state estimation
  • Experience with physics simulators and digital-twin platforms
  • Solid grounding in control, robotics or systems engineering
  • Ability to evaluate models against field data and quantify uncertainty
  • Clear written and spoken communication with non-specialists

Nice to have

  • Experience with robotics simulation environments
  • Background in verification or validation of safety-relevant systems
  • Experience with industrial sensor and telemetry data

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Simulation and digital twinsAdvancedBuild useful virtual representations of environments
  • Robotics and controlAdvancedPerceive, plan and act through physical systems
  • Model evaluationWorkingMeasure capability and failure modes credibly
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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