Is there an engineer shortage?

This is a question that is asked frequently and in many different places and forums. While opinions vary as to whether the profession is hemorrhaging talent or the candidate pool is just a little anemic, one recruiting firm is revisiting the question from a different angle.  

To wit, the question is less about numbers and skillsets and more about clarity regarding a given role being recruited, says Matthew Besford, Recruitment Specialist-Machine Vision and Metrology for Marconi Associates, a UK-based technical recruiting firm.

Indeed, as AI, robotics, and machine vision continue to reshape manufacturing, demand for technical talent remains high. But the industry's hiring challenge is often misunderstood, Besford says. The issue is not a simple shortage of engineers in general, but rather of professionals who combine imaging expertise with practical knowledge of optics, lighting, controls integration, robotics, and customer-facing deployment experience.

At the same time, the market is changing. Machine vision activity is rebounding, AI-related skills are becoming increasingly valuable, and employers are competing for specialists whose expertise spans multiple disciplines.

So, where do companies find the right candidates? How do they attract—and probably even more important— keep them?

Vision Systems Design wanted to learn more, so we reached out to Besford to hear more about Marconi Asociates’ approach to recruitment and retention.

Editor's note: The following Q&A may have been edited for style and/or clarity.

Vision Systems Design (VSD): First, is there an engineer/talent shortage? What is the job market doing, and how has candidate behavior changed?

Matthew Besford (MB): In U.S. machine vision, I would describe it as a shortage of particular combinations of skills rather than a blanket shortage of engineers. Finding someone who understands machine vision is one thing. Finding someone who also understands cameras, optics, lighting, industrial controls, and how to get a system working reliably on a customer’s production line is much harder. That person can be attractive to a vision manufacturer, a systems integrator, a robotics company and the manufacturer buying the equipment.

From a recruitment perspective, the question is where that activity creates additional pressure on applications, integration, and customer delivery teams. When speaking with candidates, I would focus on how carefully they assess the move. For a U.S. field applications or integration engineer, “remote” can still mean substantial time in airports and customer plants. Travel frequency, short-notice assignments, weekend commissioning, and available support matter alongside salary. Healthcare costs, retirement benefits, and business stability also need to be understood. A higher base salary alone may not make the overall opportunity better.

VSD: Where are companies finding qualified machine vision candidates? Are more becoming available?

MB: I would map the U.S. talent pool across four groups: vision technology suppliers, integration and distribution partners, machine builders and robotics integrators, and manufacturers with internal automation teams. That includes people working with platforms from businesses such as Cognex, Keyence, Teledyne, and other imaging suppliers.

But knowing a brand is only the starting point. We need to understand what someone actually did with the technology. An engineer who has delivered inspection applications in automotive, medical devices, electronics, or food and packaging may have highly relevant experience without holding a machine vision job title. Likewise, someone working on vision-guided robotics or warehouse identification systems may bring transferable skills.

Geography becomes important because the candidate needs to be close enough to the team or customer base or willing to travel or relocate. A national search can look healthy until you apply the location and travel requirements.

I would be cautious about saying there is an increase in qualified machine vision candidates without specific evidence. More available software engineers don’t necessarily mean more engineers who can commission an industrial vision system.

VSD: What matters to companies filling roles, and what mistakes do they make?

MB: For a U.S. machine vision business, the hire usually needs to improve something tangible, such as application feasibility, deployment speed, inspection reliability, or customer support.

The first mistake is failing to distinguish the role. An algorithm developer, an applications engineer, and a commissioning engineer contribute differently. Combining all three into one job description can create a very small candidate pool and unrealistic expectations.

The second is hiring too narrowly around a specific platform. Direct product experience is useful, particularly when someone needs to contribute immediately, but employers should also assess the underlying engineering knowledge and ability to learn.

A more serious mistake is selling a role as product development when much of the work is customer troubleshooting and travel. Another is expecting one experienced engineer to support sales, design the solution, commission it, and handle every escalation without sufficient resources.

Those problems affect retention as well as recruitment. If the same person becomes the answer to every difficult customer problem, the company creates both burnout risk and a dependency on one individual.

VSD: What is more challenging: finding talent or keeping talent?

MB: Finding an experienced U.S. machine vision engineer can be the immediate challenge. Keeping them becomes more difficult if their expertise makes them indispensable to every project. Someone who can solve difficult inspection or vision-guided robotics problems will have transferable value across multiple employers.

Retention needs to address the work itself. Are they developing technically? Do they have time to engineer a robust solution? Is travel manageable? Is there someone else who can take over a customer escalation?

We then help the company explain why an experienced candidate should join. Against an established vision business, the startup’s proposition might be direct influence over the product, ownership of the applications function, access to the founders, or the opportunity to build a team. But candidates need evidence behind the opportunity: paying customers, credible funding runway, realistic deployment commitments and clarity about technical support. Equity needs to be explained properly, and the cash compensation and healthcare package still need to work.

Marconi’s value is in connecting that brief to the wider automation talent pool, approaching relevant people directly, and assessing whether they want the realities of an early-stage business. The aim is to find someone who is motivated by the responsibility and equipped to deliver it.

VSD: What new skills are emerging with AI in automation and vision integration? What are the must haves?

MB: Companies need people who can connect AI capability with production performance. U.S. manufacturing employers are increasingly asking for AI skills.

For machine vision, I would look for imaging fundamentals, data judgement, integration capability, deployment skills, and customer communication. The exact requirements depend on the role. For example, an integration engineer does not necessarily need to train a custom neural network.

What the team must understand is that a successful demonstration is only the beginning. The system needs to work when lighting changes, products vary, or defects appear that weren’t represented in the original data.

VSD: What should the highest priorities be for recruitment and retention, and what advice would you give someone building a team in the U.S. in machine vision?

MB: Be precise about the expertise you need and realistic about the opportunity you are offering. Employers need to decide which capabilities are essential now and which they can develop.

I would prioritize role clarity, realistic working conditions, and technical development. Be explicit about how much of the role is development, application testing, commissioning, or support. Describe travel accurately. Assess engineers against real application problems and make sure the interview process gives them access to the people they will work with.

After hiring, invest in product training, mentoring, and knowledge sharing. Finally, give technical specialists a credible career path and build enough depth that customer delivery doesn’t depend on one person.

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