The sector is in a strong hiring phase. MOD expenditure with industry supported a total of 272,000 direct jobs in 2023/24, an increase of 11% compared to the previous financial year the MOD supported employment estimates for 2023/24. That builds on momentum from the year before, when the total number of MOD-supported UK jobs reached 239,000 in 2022/23, an increase of 6% on the previous financial year the MOD supported employment estimates for 2022/23.
Recruitment appetite is rising alongside the job numbers. The defence sector continued to be an attractive employer, with surveyed recruitment growing by 17.8% in 2022 a GOV.UK report on the defence sector's contribution to the UK economy. Fresh investment is adding to the pipeline too: more than 1,700 new jobs are being created across the UK defence sector following at least £1.4 billion in foreign direct investment committed since July 2024 GOV.UK figures on the defence dividend.
At the same time, demand for the AI skills that underpin modern defence systems is climbing fast. Demand for AI skills increased nearly 200 percent in a year, with London accounting for 80 percent of AI-related job postings and nearly two-thirds of all technology vacancies in the UK The Register, citing Accenture data. Put the two trends together and you can see why specialist AI data work is becoming its own hiring category.
Data labelling is the work of tagging raw inputs, such as imagery, audio, sensor feeds and text, so an AI model can learn from them. In defence AI, accuracy and context matter far more than volume: a mislabelled object in training data can skew how a system behaves later. That raises the bar for the people doing the work and for the roles built around them.
The emerging roles cluster into a few groups. Annotators and labelling specialists handle the hands-on tagging. Quality reviewers and annotation leads check consistency and resolve edge cases. Data engineers and ML operations staff build the pipelines that move labelled data into training. And domain experts, often with military or security backgrounds, provide the judgement that general annotators cannot. Many of these positions require security clearance, which narrows the available pool and lengthens time to hire.
Two dynamics drive this specialism. First, the concentration of AI roles: with London accounting for 80 percent of AI-related job postings The Register, citing Accenture data, employers outside the capital compete hard for the same scarce skills. Second, the scale of the sector itself, with MOD expenditure supporting 272,000 direct jobs in 2023/24 the MOD supported employment estimates for 2023/24, means demand is spread across primes, SMEs and the supply chain.
Start with the clearance reality. Map which roles genuinely need clearance and which can begin before it completes, so you are not holding up every hire on the same bottleneck. Build a realistic timeline with the person from the first conversation, and keep them warm through the process.
Be precise about the skills. Data labelling for defence AI blends attention to detail, domain knowledge and a feel for how models learn. Write job descriptions that separate the must-haves from the nice-to-haves, and test for judgement, not just tool familiarity. Many strong hires come from adjacent fields such as intelligence analysis, GIS or quality assurance, so widen your search beyond people who already carry the exact job title.
Move quickly once you find the right person. With recruitment across the sector growing by 17.8% in 2022 a GOV.UK report on the defence sector and fresh investment creating more than 1,700 new jobs GOV.UK figures on the defence dividend, good people have options. A clear, respectful and prompt process is often what wins them.
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