What is Deep Tech?
We sure like to talk about it here at DeepRec.ai, but what is it really?
Deep Tech refers to cutting-edge technology that’s built on major scientific or engineering advancements.
It usually takes a huge amount of investment and research to bring it to life, but when it happens, it tends to change the way the world works.
Think ChatGPT, quantum computing, reusable rockets, or even the robot dogs from Boston Dynamics.
- What we call Deep Technology one day could be everyday items the next. Ten years ago, AI-powered image recognition was considered Deep Tech. Today, it’s the same technology that powers common features, like tagging on social media or the lock on your smartphone.
Some of the key areas in Deep Tech include:
Every big breakthrough starts here. From theoretical concepts to applied experiments, research powers progress across the full deep tech spectrum.
Natural Language Processing (NLP)
NLP involves teaching computers to understand and generate human language. This covers everything from voice assistants to the Large Language Models (LLMs) that burst onto the global stage a few years back.
Whether it's text, images, audio, 3D models, or code, GenAI refers to AI systems that create content. It’s one of the fastest-growing areas of Deep Tech. Valued at $45.6 billion in 2024, the GenAI market is expected to exceed $1 trillion by 2032, growing at an annual rate of nearly 47%.
Machine learning is a field of AI focused on building systems that can recognise patterns in massive datasets, make decisions, and improve their performance autonomously. You can find this technology in personalised recommendations (like your ‘for you’ feed on Netflix), fraud detection systems in banks, and even robot vacuum cleaners.
Computer vision is the process of teaching machines to interpret and understand visual information, from images and videos to real-world environments. It’s the tech behind self-driving cars, medical imaging, and automated checkout systems. If machine learning is the brain, computer vision is the eyes feeding it what it needs to think.
Embedded Engineering is all about building systems that live in hardware. Sensors, wearable tech, robots, smart devices – these are systems that need to perform specific tasks, often with limited memory and power. They need to be ultra reliable because they’re often operating in high-risk environments, in real-time. This includes braking systems, UAVs, and MedTech.
Check out our live Deep Tech Roles:
How Does it Differ from Regular Tech?
‘Regular tech’ generally involves applying existing technologies to make improvements to new products, systems, and initiatives, whereas Deep Tech focuses on using engineering or scientific breakthroughs to move beyond the limits of what’s possible with technology.
This also means that development cycles and maturity curves tend to be much longer in Deep Tech, where years of research are often required before a viable product exists.
That said, the hype surrounding the industry has intensified to a point where investors are betting big on ideas with no business model, as seen in the recent $2 billion seed raise (the largest in history) for Thinking Machines Lab, a startup without revenue or product yet, and valued at $12 billion.
Much of Deep Tech is also hardware-centric, with much of its capital going to custom processors, robotics, or embedded systems that operate in physical environments.
TL;DR
Regular tech builds on existing tools to improve everyday products. Deep Tech pushes scientific boundaries, often involves hardware, and takes years to develop, which lays the groundwork for the tech we use every day.
Why Deep Tech Matters Right Now
In many ways, Deep Tech is the backbone of the next industrial revolution. It’s transforming the infrastructure we depend on for critical sectors, including healthcare, financial services, defence, energy, manufacturing, and AI.
The advancements made in today’s Deep Tech space are impacting the way we live and work. From the smallest AI-powered chatbot to drug discovery systems in oncology, this technology is challenging our perceptions of what it means to interact with the world.
Investors are aware of Deep Tech’s unparalleled commercial potential. The numbers speak for themselves:
- According to Dealroom, 33% of global VC funding went to Deep Tech in 2024.
- In March 2025, OpenAI completed the largest VC funding round in history, raising $40 billion in funding.
- 60% of all VC in Switzerland went to deep tech in 2025, the highest national share in the world.
- Deep Tech companies are around 10% more likely to become unicorns, as per info from Silicon Roundabout Ventures Community.
- Tech giants have spent over $155 billion on AI at the halfway point of 2025.
Deep Tech isn’t hiding on the fringe anymore. It’s the home of the biggest bets and breakthroughs, but it’s suffering from a host of unique challenges, including major infrastructure and energy demands, regulatory complexity, and commercialisation hurdles.
Out of all these challenges, a lack of access to skilled candidates remains one of the most enduring. Today’s deep tech pioneers are competing for incredibly rare expertise, and the demand is only increasing.
Why is Deep Tech Recruitment Different?
When you’re working at the bleeding edge of technology and discovery, the number of people available to perform the jobs is naturally going to be smaller.
Technological advancements are moving so quickly that formal training and academia are struggling to keep up. This means there are fewer people in the pipeline, which ultimately leads to perpetual skill shortages.
Take emerging technologies like quantum computing, for example, an area that demands expertise in quantum physics, advanced mathematics, and hardware engineering.
Not many people fit this profile, and the ones that do are worth a great deal of money – you can now expect to pay a 5% premium on someone’s salary if they have machine learning experience.
Plus, if they’re founding engineers (one of the first technical hires you make as a startup), businesses often end up offering an additional 20% equity.
It’s a similar story in the majority of Deep Tech categories: there aren’t that many people with the right mix of skills, and the competition to hire them is fierce, which drives up prices.
Hiring managers, therefore, are often stuck between needing highly specialised people to move the product forward and a complex market with shallow talent pools.
Deep Tech Hiring Challenges
Industry/Academia Skills Mismatch
Subjects are taught linearly, but in industry, especially in Deep Tech, progress often comes from the overlap between disciplines. Engineers are expected to think across systems, not just within silos.
Cross-Industry Competition
As AI adoption becomes increasingly popular across a range of industries, hiring managers often find themselves competing for talent with non-tech-native businesses like financial services and healthcare.
Industry readiness gaps
Many technically brilliant candidates come from academic backgrounds and may have limited industry experience in product-focused or commercial environments.
Multi-disciplinary role requirements
Roles in Deep Tech frequently span multiple fields, like software, physics, hardware, and life sciences, which narrows the candidate pool even further.
To cap it all off, the industry suffers from feeble retention rates, especially when it comes to machine learning and natural language processing.
The talent here moves at warp speed, and it’s unsurprising when there are so many options available to the top candidates.
Some of those opportunities are breaking records too. Meta recently offered an AI researcher a $250 million remuneration package, and while this is an outlier, it spotlights how far businesses will go to dominate the AI arms race.
All of this makes Deep Tech hiring a high-stakes process. Specialist recruitment agencies are uniquely positioned to make a real difference.
Is There a Way Around It?
Yes! At DeepRec.ai, it’s our job to connect companies with the right people at the right time, and that means thinking differently about recruitment.
Speed and precision are always the baseline, but in Deep Tech, they’re not quite enough on their own. A nuanced understanding of the science behind the tech, market activity, investment flows, and research projects is essential.
Without it, hiring managers and recruiters won’t be able to speak the same language as the engineers, researchers, and founders shaping deep tech.
Traditional Recruitment Methodologies Won’t Cut It
Hiring in Deep Tech demands a different playbook. Standard approaches (keyword CV searches, generalist outreach, or transactional processes) rarely land the people who can design quantum algorithms or optimise neural nets.
To succeed, hiring managers need to rethink their approach. At DeepRec.ai, four of the most impactful methods we’ve used include:
1. Community-Driven Hiring
As we mentioned previously, you’ll often find the best candidates are already working in the industry. Accessing these people through traditional recruitment is tough, which is partly what inspired our community-building approach (alongside our love of Deep Tech).
Through our global networking initiatives, podcast series, conferences, and panel discussions, we’ve built up an engaged and highly qualified community of deep tech professionals.
This means we can identify and connect with passive candidates who are rarely visible through traditional channels, while at the same time, positioning ourselves as a trusted partner in the deep tech ecosystem.
By fostering genuine relationships in this way, we create a talent pipeline that feels organic to candidates and gives our clients access to expertise that would otherwise be out of reach.
2. Cross-Border Expertise
The best candidates tend to be spread out across a range of global hubs. Talent mobility is vital to the success and growth of Deep Tech, but moving people between jurisdictions can be a complex and costly process.
To address this, we’ve invested in SECO and AUG licensing, backed by a dedicated in-house compliance team.
This allows us to support smooth cross-border hiring, such as relocating engineers from Japan to the US, without adding friction for clients or candidates.
3. Multi-Sector Expertise
Deep Tech breakthroughs frequently overlap with a range of sectors and disciplines, which often include highly regulated sectors like financial services, pharmaceuticals, and healthcare.
This creates an additional challenge: how do you find niche deep tech talent with a comprehensive understanding of industry-specific requirements?
A generalist approach risks missing this nuance. The DeepRec.ai team draws on Trinnovo Group’s full spectrum of expertise, leaning on our sister brands: Broadgate, Trust in SODA, and Sorai to access specialists in adjacent fields.
Learn more about our sister brands:
Broadgate – Financial Services Recruitment
Risk, Legal, Compliance, Financial Crime, Accounting, Sales & Relationship Management, Transformation & Change
Trust in SODA – Tech Recruitment
Software Engineering, DevOps, Cloud & Infrastructure, Data, Go-to-Market
Sorai – Strategic Design & AI Product Consultancy
4. Dedicated Delivery Teams
Frontier tech roles are often so specialised that even understanding the job description can be a challenge without prior exposure to the field. Traditional generalist recruiters may struggle to identify the right skills or evaluate candidate fit at this level of granularity.
That’s why we use specialist delivery teams who focus exclusively on Deep Tech mandates.
Their familiarity with the technical skills, industry standards, and candidate expectations enables a much more credible hiring process, one that resonates with the talent and ensures the right match for clients.
Support from DeepRec.ai
Whether you’re navigating the Deep Tech talent shortage, searching for a rewarding new career in AI, or simply want to learn more about the world’s most impactful industry, DeepRec.ai are always here to help.
If you want to hire amazing people:
Talk to a recruitment consultant
If you’re searching for a new job:
Browse our live deep tech roles
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