The AI industry is growing faster than it can staff itself. Companies across every sector are racing to build AI capabilities. The thing is that the talent pool has not kept pace. For every machine learning engineer on the labor market, multiple companies are competing to hire them.
That's the reality for AI companies right now. Every business with an AI roadmap is chasing the same small pool of machine learning (ML) engineers, Natural Language Processing (NLP) specialists, and generative AI developers. The demand is staggering. The supply? Not even close.
The numbers tell a stark story. By some estimates, there are ten open AI roles for every qualified candidate. Companies are stuck in a strange paradox: they have funding, vision, and market opportunity. What they don't have is the talent to build what they imagine.
This labor shortage is not just slowing growth. It is fundamentally reshaping how companies think about hiring. The old playbook of posting jobs and hoping for local talent doesn't work anymore. The new approach? Looking beyond borders and hiring internationally. That's where platforms like Pebl and the EOR model come in.
Understanding the global AI talent shortage
The AI talent gap is not a future problem. It's happening right now. The demand for AI expertise has outpaced supply so dramatically that even well-funded companies with strong employer brands are struggling to fill critical roles.
The supply-demand gap
"AI is at the forefront of corporate transformation, but without the right talent, businesses will struggle to move from ambition to implementation," said Sarah Elk, Partner at Bain & Company. Demand for AI talent has increased 21% annually since 2019, yet the AI talent shortage is likely to persist through 2027, the company reports.
Top talent tends to cluster in a handful of cities and countries. Silicon Valley, London, Toronto, and a few other tech hubs hold a disproportionate share of experienced ML engineers. Many of these professionals are already locked into lucrative positions at big tech companies or academic institutions. Even mid-sized AI companies with solid funding find themselves outbid or overlooked.
"This demand far outpaces the supply of qualified professionals, creating a projected 50% hiring gap, intense employer competition, and a clear seller's market," reports Keller International.
Areas most affected
Machine learning engineers top the list of hardest-to-fill roles. Right behind them are specialists in NLP and large language models (LLMs), the architects behind conversational AI and generative systems. Data scientists with hands-on AI implementation experience are also scarce. Companies need people who can move beyond theory and ship products.
MLOps engineers and AI infrastructure specialists are equally hard to source. These roles did not exist in large numbers five years ago. Now they are essential for scaling AI systems in production. The talent pipeline has not caught up to the infrastructure demands of modern AI development.
According to a 2025 AI jobs report by Autodesk, "A new class of roles is emerging-and they're not all technical. Positions like AI Engineer (+143.2%), Prompt Engineer (+135.8%), and AI Content Creator (+134.5%) are among the fastest growing this year."
Leadership teams also struggle to confidently deploy AI systems as a result of insufficient talent. Bain's research reveals that nearly half (44%) of executives report a lack of in-house AI expertise as a fundamental barrier to implementing generative AI.
Hiring timelines and opportunity costs
The average time to hire an ML engineer in competitive markets exceeds 45 days. That timeline stretches even longer for senior roles or niche specializations. Every week a position stays open means delayed product launches, slower iteration cycles, and mounting pressure on existing teams.
The costs compound quickly. Internal recruiters and global hiring managers spend countless hours sourcing candidates, reviewing portfolios, and conducting technical interviews. Engineering teams get pulled away from core work to participate in hiring loops. Meanwhile, competitors who moved faster are already shipping features and capturing market share. The opportunity cost of slow hiring is not just financial, it's strategic.
Why global hiring is the answer
The AI talent shortage feels unsolvable when companies limit themselves to local markets. But the problem becomes manageable when the search expands globally. The talent exists. It just lives in different places.
Untapped talent in emerging markets
High-quality AI and machine learning engineers are working in India, Brazil, Eastern Europe, and Southeast Asia right now. Many of these professionals have strong technical foundations, hands-on experience with modern AI frameworks, and the specialized skills companies desperately need. Yet traditional recruiting tools and strategies often overlook them entirely.
These regions produce thousands of capable engineers each year. They graduate from respected universities, contribute to open-source projects, and build real AI applications. The issue is not talent quality. The issue is visibility and access. Companies that limit their search to familiar markets miss out on engineers who could start contributing immediately.
Advantages of going global
Less-saturated markets mean faster hiring cycles. While U.S.-based ML engineers juggle multiple offers and drawn-out negotiations, talented professionals in other regions are ready to move quickly. Companies that hire globally can fill positions in weeks instead of months.
Access to specialized skill sets improves dramatically when the candidate pool expands across continents. Need someone with expertise in a specific AI domain or a niche framework? The odds of finding that person increase when the search includes global markets.
Cost efficiencies are another major advantage. Competitive salaries in emerging markets often represent significant savings compared to Silicon Valley rates, without any compromise on technical ability or output quality.
Barriers to global hiring
The challenge is not finding the talent. The challenge is the operational complexity of hiring internationally. Legal and compliance requirements vary wildly by country. What works in one jurisdiction can create a serious risk in another.
Local employment laws, tax obligations, and statutory benefits differ everywhere. Companies need to navigate visa requirements, labor contracts, and termination protections specific to each market. Then there are the logistics: setting up payroll in multiple currencies, managing benefits packages across different healthcare systems, and onboarding employees who may never set foot in a home office.
These obstacles stop many companies from hiring globally, even when they know the talent is out there. This is exactly where an Employer of Record like Pebl steps in to remove the friction.
The role of EORs in scaling AI teams
An Employer of Record removes the operational barriers that prevent companies from hiring globally. Platforms like Pebl act as the legal employer for international team members. They handle employment contracts, payroll processing, benefits administration, and compliance across 185+ countries.
The traditional alternative requires setting up a legal entity in every country where a company wants to hire. That process can take months and involves significant legal costs and ongoing administrative overhead. An EOR eliminates that entirely. Companies can hire in new markets within days instead of months.
The impact EORs have on AI hiring timelines is substantial. Legal risk drops because the EOR stays current on local labor laws, tax requirements, and employment regulations. HR teams stop juggling multi-currency payroll systems and navigating global compliance issues.
Engineering leaders can focus on building products and growing their teams instead of managing international HR logistics. For AI companies racing to scale, that shift in focus can be the difference between leading a market and falling behind.
How Pebl solves the AI talent shortage
Pebl was built specifically to address the friction between finding global talent and actually hiring them. The platform combines AI-powered tools with full-service EOR support to help tech companies scale their teams without the usual roadblocks.
- AI-powered talent matching. Pebl matches companies with pre-vetted machine learning engineers across global markets.
- Built-in collaboration for fast hiring decisions. Engineers, recruiters, and hiring managers work together on one platform. Shared shortlists, comments, and decision tracking eliminate bottlenecks and keep hiring momentum moving.
- EOR support to hire anywhere, fast. Pebl's global EOR services allow companies to compliantly hire top ML engineers in any country without legal friction. Full lifecycle support includes onboarding, payroll, benefits, compliance, and retention.
- Time-to-hire cut from weeks to days. Instead of 45+ days to hire an ML engineer, Pebl customers can onboard talent in as little as 48 hours in many markets. Teams stay focused on innovation, not recruiting logistics.
The AI talent shortage will not disappear anytime soon. But the hiring delays, legal friction, and missed opportunities can.
Pebl helps companies build ML teams faster by removing the barriers that slow down global hiring, offering compliance confidence and onboarding timelines measured in days instead of months. When the best engineer for the role lives halfway around the world, the companies that win are the ones that can hire without borders and scale without delay.
Contact Pebl today to book a demo or learn more about its EOR services.
Disclaimer: This information does not, and is not intended to, constitute legal or tax advice and is for general informational purposes only. The intent of this document is solely to provide general and preliminary information for private use. Do not rely on it as an alternative to legal, financial, taxation, or accountancy advice from an appropriately qualified professional. The content in this guide is provided "as is," and no representations are made that the content is error-free.
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Topic:
HR Strategies
