The AI future has arrived. From tech and finance, to healthcare, retail, and manufacturing, nearly every industry today has begun to incorporate artificial intelligence (AI) into their technology platforms and business operations. The result is a surging talent demand for engineers who can design, implement, leverage, and manage AI systems.
Over the next decade, the need for AI talent will only continue to grow. The US Bureau of Labor Statistics expects demand for AI engineers to increase by 23 percent by 2030 and demand for machine learning (ML) engineers, a subfield of AI, to grow by up to 22 percent.
In the tech industry, this demand is in full swing. Job postings that call for skills in generative AI increased by an incredible 1,848 percent in 2023, a recent labor market analysis shows. The analysis also found that there were over 385,000 postings for AI roles in 2023.
Figure 1: Growth of job postings requiring skills in generative AI, 2022-2023
To capitalize on the transformative potential of AI, companies cannot simply hire new AI engineers: there just aren’t enough of them yet. To address the global shortage of AI engineering talent, you must upskill and reskill your existing engineers.
Essential skills for AI and ML
AI and its subfields, machine learning (ML) and natural language processing (NLP), all involve training algorithms on large sets of data to produce models that can perform complex tasks. As a result, different types of AI engineering roles require many of the same core skills.
CodeSignal’s Talent Science team and technical subject matter experts have conducted extensive skills mapping of AI engineering roles to define the skills required of these roles. These are the core skills they identified for two popular AI roles: ML engineering and NLP engineering.
Machine learning (ML) engineering core skills
Natural language processing (NLP) engineering core skills
Developing AI skills on your teams
A recent McKinsey report finds that upskilling and reskilling are core ways that organizations fill AI skills gaps on their teams. Alexander Sukharevsky, Senior Partner at McKinsey, explains in the report: “When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half of the companies we surveyed are doing so.”
So: what is the best way to develop the AI skills you need within your existing teams? To answer that, we first need to dive deeper into how humans learn new skills.
Components of effective skills development
Most corporate learning programs today use the model of traditional classroom learning where one teacher, with one lesson, serves many learners. An employee starts by choosing a program, often with little guidance. Once they begin the course, lessons likely use videos to deliver instruction and are followed by quizzes to gauge their retention of the information.
There are several problems with this model:
- Decades of research show that the traditional, one-to-many model of learning is not the most effective way to learn. Educational psychologist Benjamin Bloom observed that students who learned through one-on-one tutoring outperformed their peers by two standard deviations; that is, they performed better than 98 percent of those who learned in traditional classroom environments. The superiority of one-on-one tutoring over classroom learning has been dubbed the 2-sigma problem in education (see Figure 2 below).
- Multiple-choice quizzes provide a poor signal of employees’ skills—especially for specialized technical skills like AI and ML engineering. Quizzes also do not give learners the opportunity to apply what they’ve learned in a realistic context or in the flow of their work.
- Without guidance grounded in their current skills, strengths, and goals—as well as their team’s needs—employees may choose courses or learning programs that are mismatched to their level of skill proficiency or goals.
Figure 2: Comparison of the distributions of student performance by instructional style shows a 2 sigma difference in mean performance scores.
Developing your team members’ mastery of the AI and ML skills your team needs requires a learning program that delivers the following:
- One-on-one tutoring. Today’s best-in-class technical learning programs use AI-powered assistants that are contextually aware and fully integrated with the learning environment to deliver personalized, one-on-one guidance and feedback to learners at scale.
The use of AI to support their learning will come as no surprise to your developers and other technical employees: a recent survey shows that 81 percent of developers already use AI tools in their work—and of those, 76 percent use them to learn new knowledge and skills.
- Practice-based learning. Decades of research show that people learn best with active practice, not passive intake of information. The learning program you use to level up your team’s skills in AI and ML should be practice-centered and make use of coding exercises that simulate real AI and ML engineering work.
- Outcome-driven tools. Lastly, the best technical upskilling programs ensure employees actually build relevant skills (not just check a box) and apply what they learn on the job. Learning programs should also give managers visibility into their team members’ skill growth and mastery. Your platform should include benchmarking data, to allow you to compare your team’s skills to the larger population of technical talent, as well as integrations with your existing learning systems.
Deep dive: Practice-based learning for AI skills
Below is an example of an advanced practice exercise from the Introduction to Neural Networks with TensorFlow course in CodeSignal Develop.
Example practice: Implementing layers in a neural network
In this practice exercise, learners build their skills in designing neural network layers to improve the performance of the network. Learners implement their solution in a realistic IDE and built-in terminal in the right side of the screen, and interact with Cosmo, an AI-powered tutor and guide, in the panel on the left side of the screen.
Practice description: Now that you have trained a model with additional epochs, let’s tweak the neural network’s architecture. Your task is to implement a second dense layer in the neural network to potentially improve its learning capabilities. Remember: Configuring layers effectively is crucial for the model’s performance!
Conclusion
The demand for AI and ML engineers is here, and will continue to grow over the coming years as AI technologies become critical to more and more organizations across all industries. Companies seeking to fill AI and ML skills gaps on their teams must invest in upskilling and reskilling their existing technical teams with crucial AI and ML skills.
