It’s almost impossible to get a job in artificial intelligence right now

In recent years, the topics of artificial intelligence and machine learning have become very popular and have become established topics of discussion in most households and workplaces. There is no doubt that the industry has grown exponentially in a relatively short period of time, buoyed by numerous small and large players actively trying to gain market share.

Alongside this explosion of growth has emerged a tremendous need for the right talent and workforce to design and develop these products. But despite the demand, the standards for a job in artificial intelligence and machine learning have never been stricter. This is especially true for AI jobs in healthcare.

Research shows that, conservatively speaking, the AI ​​market is expected to reach nearly $267 billion by 2027, with an annual growth rate of nearly 37%. In particular, the market capitalization for AI in healthcare is expected to increase more than 10-fold in the next 8 years. Accordingly, the numbers also show that jobs related to AI and ML have grown by almost 74% annually over the last four years, suggesting that the market is hungry for talent to meet innovation needs.

However, these numbers might mislead a healthcare AI enthusiast into thinking that there are many job openings and many opportunities for new entrants to work in this field. That couldn’t be more wrong. In fact, the standards for hiring in the fields of artificial intelligence and machine learning have become increasingly difficult, despite the high demand for talent in these fields in the current workforce. Candidates for this work are expected to have a combination of expertise from a wide range of disciplines, ranging from programming and IT architecture to at least a basic understanding of computational engineering and neural networks. In addition, the senior positions leading product teams or actually overseeing the development of basic and large-scale language models are increasingly being given to academics and industry heavyweights who have previously carried out the work in a research or more academic capacity. This is particularly true in healthcare, where complex industry knowledge is required to apply the basic principles of AI.

For example, Isomorphic Labs and Google DeepMind, both Alphabet companies, have been working at the intersection of healthcare, technology and biology for many years. This work recently announced the latest version of the AlphaFold tool, which leverages the best of machine learning and advanced fundamental models to advance the fields of proteomics, chemistry and biology. While it may seem like an overnight success, the work that produced this innovation took years, if not decades, to complete and originated and originated in research laboratory environments. Accordingly, the leadership behind much of the work reflects a highly academic and research-oriented pedigree.

Another example is Apple and its work in healthcare. Although Apple is just becoming a recognized giant in artificial intelligence related to its consumer health product range, the company has for many years relied on the talent of doctors and healthcare experts to help develop technology and workflows. With this foundational knowledge, experienced experts with both academic and commercial backgrounds are now required to bring this foundational work together and integrate it with usable artificial intelligence, models and consumer products to ultimately turn ideas into viable products.

Therefore, despite the growth and increased demand, companies remain extremely selective in hiring candidates to work in this field, making this one of the most competitive career fields to date. Without the right mix of research, experience, and expertise in the field, getting a job in AI is nearly impossible. In fact, “research shows that the majority of business leaders (66%) will not consider hiring a candidate who does not have AI skills… 71% of executives say they prefer to hire a candidate with AI skills, “even if they have AI skills.” less experience, compared to a more experienced candidate who lacks the aptitude for AI.”

Given the growth of these fields, the future for jobs in AI and ML is bright. Educators, innovators and institutions alike must be prepared to meet the demands and needs of a modern workforce.