Hiring nowDefence and national security AI assurance

Defence AI Systems Engineer

Also called Defence AI Engineer

A Defence AI Systems Engineer integrates AI components into authorised mission systems and proves they work in context. They make sure the system stays reliable and inside approved boundaries, which is what customers and assurers need to see.

What they do

The work starts with an AI component that performs well on its own and has to function inside a larger mission system. The engineer defines the interfaces, data flows and fallback behaviour. They build test scenarios in authorised simulation, often with digital twins, and run the integrated system against them. They record where it fails, work with model teams on fixes, and check each change against approved boundaries. They also work with security and assurance colleagues, using secure analytics and assurance tooling to produce evidence that others can review and challenge.

What a strong hire delivers

In the first months, a strong hire maps how the AI component connects to the wider system and where it can fail. They set up a repeatable simulation test set tied to mission context. They deliver an integration that behaves predictably, plus evidence a reviewer can follow from requirement to result. They also agree clear limits on what the system may and may not do, and make sure those limits are tested.

How you can tell they are good

Ask them to walk through an integration they delivered, from interfaces to evidence. Good candidates explain what broke, how they found it and what they changed. They separate model performance from mission reliability. They can describe how they tested boundaries, not just accuracy. Be wary of anyone who talks only about models, or treats assurance as paperwork done at the end.

Why it is hard to fill

Few people sit across both AI and mission systems. Most have grown up in one. Defence systems engineers may have little model experience, while ML engineers rarely know verification or secure environments. Titles vary widely, and much of the best work cannot be described openly. Eligibility and clearance requirements narrow the pool further, so candidates often need to be found through adjacent disciplines.

Where to find them

Look at systems integration engineers, simulation and modelling engineers, security analysts and safety assurance specialists who have moved toward AI. They tend to come from defence primes, specialist defence technology firms, government-linked research bodies and assurance consultancies. Some come from regulated sectors with similar verification discipline, such as aerospace and critical infrastructure.

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 Defence AI Systems Engineer

  1. Describe a time you integrated a new component into a larger system. What failed first?

    A strong answer names a specific failure, how they traced it and what they changed. They discuss interfaces and dependencies. Worry if they only describe success or cannot say what they would do differently.

  2. How would you test an AI component for mission reliability, as opposed to model accuracy?

    They should talk about mission scenarios, edge cases, degraded inputs and fallback behaviour. They should explain why accuracy alone misleads. Worry if they only cite benchmark scores.

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

Job description you can post

We are hiring a Defence AI Systems Engineer to integrate AI components into authorised mission systems. You will work with model, security and assurance colleagues to make sure what we build is reliable, stays within approved boundaries and comes with evidence others can trust.

What you will do

  • Integrate AI components into authorised mission systems
  • Define interfaces, data flows and fallback behaviour
  • Build and run mission-context tests in authorised simulation
  • Produce clear, traceable integration and test evidence
  • Work with model teams to diagnose and fix failures
  • Check changes against approved boundaries and security requirements

What you will bring

  • Demonstrated delivery of integrated systems with verification evidence
  • Experience in defence systems, security analysis, simulation or safety assurance
  • Working knowledge of model evaluation methods
  • Ability to design tests around mission scenarios
  • Clear written communication of evidence and limits
  • Comfort working in secure, controlled environments

Nice to have

  • Experience with digital twins
  • Background in AI risk management
  • Existing eligibility to work on sensitive programmes

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

Skills that matter

  • AI risk managementAdvancedIdentify, assess, treat and monitor AI risks
  • Model evaluationAdvancedMeasure capability and failure modes credibly
  • Security engineeringWorkingProtect systems, identities and sensitive assets
  • Simulation and digital twinsWorkingBuild useful virtual representations of environments

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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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