The Deep Tech Talent Shortage Myths

Why You're Looking in the Wrong Places

Every week, another headline announces a deep tech talent crisis. "AI Engineers Command $500K Salaries!" "Quantum Experts Rarer Than Unicorns!" "Biotech Startups Can't Find PhDs!" The narrative is so widespread that hiring managers have accepted it as fact, leading to inflated salaries, sluggish hiring cycles, and a collective tunnel vision.

Here’s what’s really happening: the shortage narrative thrives because most companies are searching in the same places, for the same profiles, with the same assumptions. Meanwhile, the talent they need is solving complex problems elsewhere, unnoticed.

Myth 1: The Best Talent Is Already on LinkedIn

If your hiring process starts and ends with LinkedIn, you're missing the majority of the global technical workforce. The most valuable candidates, the ones solving real-world challenges in logistics, energy, manufacturing, or defense, often aren’t polishing online profiles. They're not chasing clout. They’re solving complex, unsung problems daily.

Instead of searching "machine learning engineer" and finding the same 200 profiles everyone else is messaging, go deeper. The mathematician working on supply chain algorithms in the Netherlands. The materials scientist in a German manufacturing firm. These professionals aren’t waiting for your message; they’re waiting to be seen.

Myth 2: Only Top Pedigrees Deliver Results

Too many hiring managers turn job specs into academic wishlists: "PhD from a top-10 university, 5+ years at a Big Tech company, experience with our exact stack."

This isn't recruiting. It's collecting trophies.

Take Sarah, a geophysicist who spent years developing seismic algorithms for energy exploration. She was passed over by a climate tech startup because she lacked "machine learning" experience, despite her mathematical models being far more advanced than those used by many data scientists.

Meanwhile, companies like SpaceX and Palantir succeed by hiring physicists, defense engineers, even game developers, people with rigorous problem-solving skills, not perfect CVs.

Myth 3: Innovation Only Happens in Certain Places

The talent pool isn’t confined to San Francisco or Berlin. Innovation is happening everywhere, from Eindhoven to Warsaw, from Toulouse to Tallinn.

Eastern Europe produces algorithmic thinkers trained in first-principles logic. Canada's research labs lead globally in AI and neuroscience. Regional universities across Europe graduate thousands of highly capable engineers each year, many of whom have never been contacted by a deep tech recruiter.

If you're only hiring within your postcode, or worse, only from a handful of elite universities, you’re choosing scarcity.

Myth 4: Industry Veterans Aren’t ‘Tech’ Enough

Some of the most overlooked candidates are sitting in legacy industries, automotive, chemicals, pharmaceuticals, doing advanced work under extreme constraints. These aren’t just engineers. They’re systems thinkers with decades of experience delivering under pressure.

James, a control systems engineer in a paper mill, was passed over for robotics roles until one startup recognised that his real-time optimisation work was a perfect fit for autonomous systems.

Deep tech innovation isn’t just about knowing a specific tool. It’s about understanding how things work in the real world.

Rewriting the Deep Tech Hiring Playbook

If you want to build world-changing technology, stop looking for unicorns. Start looking for signal: mathematical fluency, systems thinking, and grit.

• Focus job descriptions on core capabilities, not trendy tools.

• Expand your search radius. Go beyond the usual cities and platforms.

• Speak the language of skill translation. Know that “signal processing” might be called “deep learning” elsewhere.

• Invest in potential. The best teams develop talent, they don’t just buy it.

  
  

Conclusion: The Talent Is Out There If You’re Willing to Look

The shortage isn’t about numbers. It’s about perception. Brilliant, capable talent exists, often in the places you least expect, with job titles that don’t fit your filters, and experiences that don’t match your assumptions.

If you want to win in deep tech, we need to stop chasing what everyone else is chasing. Start seeing what others miss. The question isn’t whether the talent exists. The question is whether you’re willing to search differently.