Mapping the global AI engineering market
A scaling platform needed to know where the world’s AI engineers really sit before committing to a hiring plan.
A Series C AI platform scaling from 120 to 300 engineers was preparing to open two new engineering hubs. Leadership needed a structured, evidence-based view of where AI and ML talent actually concentrated worldwide — before committing to a multi-year site strategy and tens of millions in infrastructure spend.
What the client was up against
The client had no structured view of where AI and ML talent actually sat. Internal assumptions pointed toward established tech centres like San Francisco and London, but these were hunches rather than evidence. Existing recruiter data was fragmented, outdated, and heavily biased toward candidates already in active circulation. With two hub locations to choose and a Board expecting defensible rationale, the team needed a complete picture of the global landscape — not a best guess.
How Audentia solved it
Audentia deployed a three-researcher team over six weeks to map the global AI engineering landscape across 14 countries and 38 cities. We profiled 2,800+ engineers by specialisation — NLP, computer vision, reinforcement learning, MLOps — along with seniority, current employer, tenure, and estimated compensation. Competitor org structures were mapped for eight direct rivals and four adjacent players, showing team sizes, reporting lines, and recent growth trajectories. Salary benchmarking by geography was layered in, and the full dataset was delivered in the client’s ATS-compatible format for immediate operational use.
Results that lasted
The map revealed an overlooked concentration of senior NLP engineers in a mid-tier European city that hadn’t appeared on any internal shortlist — ultimately selected as the second hub location. The client used the map to make 14 direct hires in the first quarter without agency involvement, saving an estimated £280K in placement fees. The dataset continues to inform workforce planning, succession decisions, and competitive intelligence 18 months later.
Engineers profiled across 38 cities
Direct hires in the first quarter
Estimated agency fee savings
Total project delivery time
The map gave us clarity we’d been guessing at for months. We stopped debating locations and started hiring.
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