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AI Talent For Hire: Distinguishing Machine Learning and Deep Learning Engineering Talent


The race for AI talent has erupted seemingly overnight. Companies are jockeying for the best talent, yet many are unable to define exactly what type of skills they need, how to compensate these engineers, or advertise the correct title to attract the right talent.

New technologies have always created a fast-paced hiring environment, but the growth expectations for these AI and Machine Learning positions is expected to increase by nearly 40% in the next 3 years alone.

Over the past few years, AI and Machine Learning have been used as a catch all for job titles. Yet, these titles may not be enough to attract the right talent in a very tight labor market.  As tech continues to develop and engineers become more specialized, organizations must also adapt and be able to articulate exactly what they are searching for.

Though often used interchangeably under the umbrella of AI, companies will need to differentiate between machine learning, deep learning, LLM, generative AI, and neural networks. According to IBM, the easiest way for organizations to understand the type of talent they need is by viewing them “as a series of AI systems, from largest to smallest, each encompassing the next.”

Recently, our team has seen an increase in hiring requests for both machine learning engineers and deep learning engineers.  Yet, for organizations using TA teams to hire for this new breed of engineers, it is often difficult to find the right way to attract talent. Up until this point many AI-based job descriptions were used as a catch all. However, there are already significant differences and a multitude of ways to approach the hiring process.

Companies will need to clearly distinguish the type of engineer that they are searching for now and in the future as AI technology evolves.

So, how do you initially attract these engineers?

  • Highly competitive base compensation that will both attract and retain talent
  • Bonuses, RSUs, Stock Options
  • Challenging and mission-driven work
  • The ability to expand their skillset
  • Top-tier, comprehensive benefits package
  • Clear, well-written job descriptions

But even with transparency and attractive packages, you may not reach the intended talent. These engineers are in high demand, scooped up by the likes of FAANG by the time they graduate or headhunted from their current positions. Their niche skills make them high-value assets that are difficult to woo into organizations without star-power.

Talener has been working with AI skilled engineers and we have watched the creation of subsets that have come out of the larger AI umbrella. Our team has built a network of machine learning engineers who are enhancing their skills to become top deep learning experts. Having these existing relationships means that we are able to bring the right talent to your company. We are a partner for your TA and HR teams that may not regularly fill technology positions with brand-new, niche skills.

Our team can guide you through the process of defining skillsets, the future of the position, how to engage this type of talent and what kind of AI talent will be the best fit for your organization.

View our recent case studies and gain an even greater perspective.