AI roles now account for 7% of technical job postings on LinkedIn, while professionals with AI engineering skills represent less than 1% of U.S. members.
We are seeing that gap firsthand.
More of our clients are looking for engineers and technical product leaders with experience across AI infrastructure, simulation, computer vision, robotics, autonomous systems, developer tooling, and model deployment.
At the same time, we are seeing more genuinely AI-native talent enter our network. These are not people who simply added AI to their profiles. They have built models, platforms, intelligent systems, and AI-enabled products in production environments.
The challenge is that “AI talent” is not one category.
A simulation engineer is different from an ML infrastructure engineer. A computer vision leader is different from an agentic systems builder.
Titles alone rarely reveal the depth or relevance of someone’s experience.
The companies hiring well are defining the technical problem first, identifying the capabilities required to solve it, and evaluating candidates based on what they have actually built.
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For professionals considering their next move, we currently have 50+ opportunities across AI, engineering, GTM, and leadership.
Source: LinkedIn Economic Graph