How to Hire AI Engineers in Days—Not Months

There's this moment happening right now in tech. Every company is either building AI or racing to catch up. And at the center of it all is a problem nobody solved fast enough: there are not enough AI engineers to go around.

Demand for machine learning talent has exploded. In the U.S., job postings requiring "generative AI" skills grew fourfold in one year, from 16,000 in 2023 to over 66,000 in 2024.

But here's the real story: companies are stuck. They find brilliant engineers in São Paulo or Warsaw or Ho Chi Minh City. Then they hit a wall of international hiring chaos that can take months to navigate.

AI hiring is different from anything HR teams have dealt with before. The talent pool is microscopic compared to the need. The technical bar is stratospheric. And the best engineers have their pick of offers, often accepting roles within 48 hours of interviewing. Speed is everything.

That's where Pebl comes in. We built an AI-powered platform that lets you hire top ML engineers globally in days instead of months. No lawyers on speed dial. No frantic calls with payroll consultants. Just fast, compliant hiring that helps AI companies move at the pace their competition demands. Because in this race, the companies that can hire faster will simply build better products first.

Key hiring challenges for AI companies

AI companies face a hiring landscape unlike anything the tech industry has seen before. The constraints are real. And they have consequences that compound fast.

Talent shortages and intense competition

The math here is brutal. According to Matt Li, a tech entrepreneur with expertise in offshore hiring, "the global AI talent shortage has reached critical levels, with demand exceeding supply by 3.2:1 across key roles. The shortage spans from technical positions like machine learning engineers to strategic roles, including AI product managers and ethics specialists."

Reports indicate there are 4.2 million AI positions sitting unfilled globally, and only 320,000 qualified developers available to fill them. Big tech companies absorb 70% of top AI talent straight from universities. VC-backed startups snap up many of the rest.

That leaves most AI companies fighting over a microscopic pool of candidates who often have six competing offers by the time you get them on the phone. Traditional job boards and recruiting agencies were not built for this level of scarcity.

Speed-to-hire matters more than ever

Here's what happens when hiring drags on. Product roadmaps stall. Features get delayed. Competitors ship first. The average time to fill an AI role has ballooned to 142 days. That's nearly five months of lost momentum for every single hire.

Top candidates accept offers within 48 hours of their final interview. If your process takes weeks to move from screen to offer, you will lose them. Engineering leads cannot spend half their time reviewing hundreds of unqualified resumes when they should be building. Speed is not a nice-to-have. It determines who wins.

Vetting and technical assessment complexity

Machine learning expertise cannot be assessed with a generic coding test. Skills in Natural Language Processing (NLP), computer vision, or fine-tuning Large Language Models (LLM) require evaluators with expertise in the field. But 87% of organizations struggle to properly screen AI talent because most recruiters lack this technical depth.

Take-home assignments that drag on for days alienate the best engineers. They have options. Long, exhausting interview loops send them running to companies with tighter processes. The challenge is finding people who can evaluate real AI competency without burning weeks or losing candidates to friction.

Best practices for hiring today's AI engineers

The way organizations hire AI engineers directly impacts how quickly they become productive contributors. Here are proven best practices for bringing machine learning talent onto global teams.

• Expand your search beyond traditional tech hubs. AI talent exists in emerging markets across India, Brazil, Eastern Europe, and Southeast Asia at competitive rates and with strong technical capabilities. Companies that limit their search to San Francisco, New York, or London miss out on highly qualified engineers who can contribute immediately.
• Use technical screening early in the process. Filter candidates through domain-relevant assessments that test actual skills in PyTorch, TensorFlow, or specific AI frameworks before scheduling interviews. Technical screenings save time and confirm that only candidates with proven technical ability advance.
• Prioritize communication skills alongside technical ability. ML engineers often need to explain complex concepts and document their work clearly. Strong communication becomes even more critical when teams work across time zones and cultures.
• Partner with an employer of record for international hires. EOR services like Pebl's handle the legal, payroll, tax, and benefits complexities of hiring across borders without requiring companies to establish entities in each country. This infrastructure allows organizations to hire top talent in days instead of months and reduces compliance risk across multiple jurisdictions.
• Build a collaborative hiring process with clear scorecards. Engineering teams, recruiters, and hiring managers should align on evaluation criteria before interviews begin. Shared feedback systems and real-time collaboration tools keep the process moving and prevent talented candidates from accepting competing offers during delays.
• Structure onboarding to accelerate time-to-productivity. New AI engineers should have development environment access, documentation, and an assigned technical buddy before their first day. Google found that engineers with onboarding buddies reached full productivity 25% faster than those without dedicated support.
• Invest in employer brand and team visibility. Top AI talent evaluates potential employers based on technical challenges, team quality, and engineering culture. Publishing technical blog posts, contributing to open-source projects, and speaking at conferences make teams more visible to the engineers companies want to attract.
• Move fast on qualified candidates. The average ML engineer receives multiple offers within days of starting their search. Companies that take weeks to make decisions lose out to competitors who can extend offers within 48 hours.

Real-world results: What teams are saying

The numbers speak for themselves. Consensys, a blockchain software company, saw its hiring lead times drop from 3-4 weeks to just 2-3 days after implementing Pebl's integration with its existing systems. That's 700% faster. Their talent acquisition lead put it simply: "When the first one went out within three days, I was like, 'Am I dreaming? This is incredible.'" How is that possible? Pebl's platform eliminates the administrative burden that typically steals hours from busy employers.

Teams are noticing the difference beyond just speed. "Over the past year, we've doubled-if not tripled-our global headcount," says David A., VP of Finance & Operations at one of Pebl's clients. "That growth has been largely thanks to our partnership with Pebl, giving us the ability to capture talent all over the world."

The technical screening capabilities are hitting differently, too. Consensys's team was "thrilled they no longer need to complete the lengthy Excel spreadsheets" and "absolutely love" having that workload automated. Meanwhile, Materialize successfully hired specialized database engineers in Germany and Spain-precisely the kind of niche AI talent that's impossible to find locally.

The combination of speed, quality, and global reach is changing how AI companies think about building their teams.

Why Pebl is built for AI companies

Pebl was created by people who understand what it takes to scale fast in tech. The company pioneered the entire EOR category 11 years ago and has served over 1,500 high-growth companies. The rebrand to Pebl in September 2025 brought an AI-first platform specifically designed for the speed and agility that AI companies demand.

The platform was optimized from the ground up for technical hiring. Engineers, ML researchers, and data scientists are exactly the roles Pebl was built to support. Where traditional EOR providers focus on generic white-collar positions, Pebl emphasizes the unique needs of tech teams who need to hire specialized talent globally without the usual friction.

Teams can scale at breakneck speed without sacrificing quality or drowning their engineering leaders in administrative work. Pebl delivers quotes and onboarding in under 24 hours, compared to the typical weeks or months with legacy providers. That velocity translates directly into faster product iteration, because every day spent waiting to hire is a day competitors spend shipping.

The fastest way to hire AI engineers in 2025

The future of hiring is platform-led, AI-powered, and speed-optimized. Companies that still rely on traditional recruiting methods will keep losing talent to those who move faster. Pebl removes the friction that slows everyone else down and puts the right candidates in front of teams in days instead of months, because in AI, the companies that hire faster simply win. Get in touch to learn more.

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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