If you are trying to build a smart product, personalize user experiences, or integrate automation into business processes, chances are you are looking to hire machine learning engineers or an AI engineer. And if you are reading this, you are probably realizing it’s not as simple as posting a job on LinkedIn and waiting for resumes to roll in. The struggle is real—and widespread. Despite the rise in AI applications across industries, finding skilled, reliable talent remains one of the top hiring challenges for tech companies today.
Why is this happening? Is it a pure supply-and-demand issue, or are hiring managers missing something critical in how they scout for talent? In this blog, we will walk through the core reasons why companies are finding it difficult to hire AI and ML engineers—and more importantly, how to fix it.
Understanding the Talent Gap and Unlocking Smarter Hiring Strategies for ML and AI Roles
1. The Demand Far Outpaces the Supply
The global AI market is expected to reach over $1.8 trillion by 2030. This explosive growth has made machine learning and AI roles some of the most sought-after positions in tech. Yet, there’s a staggering shortage of qualified candidates. According to a report by Element AI, less than 10,000 professionals worldwide have the right blend of academic rigor and real-world AI/ML experience.
This lopsided supply-demand curve makes it extremely hard for startups and even mid-sized tech companies to compete with giants like Google, Amazon, and Meta, which have deep pockets and attractive research labs. Unless companies broaden their hiring strategies or look beyond traditional talent pools, they are going to continue hitting walls when trying to hire machine learning engineers.
2. Job Descriptions That Scare Candidates Away
One of the most overlooked problems is the job posting itself. Many hiring managers ask for a laundry list of 15+ skills that no single human can reasonably possess. Requirements like “Ph.D. in AI,” “5+ years of production-level ML deployment,” and “experience with TensorFlow, PyTorch, Keras, Apache Spark, and every other tool under the sun” are intimidating—even to senior professionals.
Simplifying the job description and focusing on core, role-specific competencies can drastically improve your chances of attracting viable applicants. Consider what your actual needs are and distinguish between “must-haves” and “nice-to-haves.”
Ask yourself: Do you need someone to build models from scratch, or is your need more about model integration and performance optimization?
3. Poor Understanding of the Role Among Hiring Managers
Here’s a challenge many tech companies face: the people hiring AI engineers often don’t fully understand what these professionals do. Is machine learning engineering about data science, software engineering, or research? The truth is—it can be a mix of all three.
When non-technical recruiters or hiring managers don’t grasp the nuances, they may undervalue critical skills like data preprocessing, model validation, or ML Ops. This leads to misaligned interviews, inappropriate screening tasks, or outright rejection of qualified candidates for the wrong reasons.
The fix? Collaboration closely with the technical team leads to designing an accurate hiring roadmap. Better alignment leads to better outcomes.
4. Rigid Hiring Models That Don’t Support Remote or Contractual Work
Many companies still insist on local, full-time hires, ignoring the massive pool of skilled AI talent that prefers remote, freelance, or project-based work. This rigidity narrows your options unnecessarily.
By offering flexible roles, startups and smaller tech companies can gain access to senior talent from around the world—without having to match big tech salaries. This also opens the door to short-term experimentation, allowing businesses to scale up or down based on project needs.
And yes, more and more professionals today are looking for flexibility. According to a 2023 Stack Overflow survey, 85% of developers prefer either remote or hybrid roles.
5. Overlooking Non-Traditional Talent Sources
Not every skilled ML or AI engineer has a master’s degree from Stanford or MIT. Many have learned through bootcamps, online certifications, open-source contributions, Kaggle competitions, or even building AI-powered side projects.
Tech companies that limit their talent search to elite universities or former FAANG employees may be unintentionally closing the door on high-potential candidates.
Platforms like GitHub, Arxiv, and even AI-focused Discord servers can be great places to discover non-traditional talent. Encourage your recruitment teams to look at contributions, not just resumes.
Similarly, some AI engineers come from a CMS or web development background before pivoting into data science. Don’t ignore them. If you’re simultaneously hiring for other roles, like needing to hire CMS engineers, it may be worth exploring candidates with hybrid skills.
6. Lack of Structured Evaluation Process
One reason companies struggle to evaluate AI/ML candidates is that traditional coding tests don’t always apply. An engineer might not be an expert at writing algorithms on a whiteboard—but they may excel at feature engineering, dataset tuning, or model deployment in production.
Create role-specific technical assessments that reflect real tasks the candidate would perform. Use tools like Jupyter notebooks, Colab environments, or simulated datasets rather than generic LeetCode-style questions.
Also, consider including a paid take-home project. It not only helps you gauge skill but also shows the candidate that you value their time.
7. Failing to Market the Opportunity Effectively
AI engineers today are not just looking for a paycheck. They are looking for interesting challenges, access to clean datasets, opportunities for learning, and a chance to make an impact.
If your job description reads like a tax document, you are doing it wrong. Highlight the exciting parts of the role. Is the team working on predictive maintenance for health tech? Personalizing content for 10 million users? Reducing latency in NLP tasks?
Your job ad should inspire curiosity, not just list demands. Remember, you are not just evaluating talent—they are evaluating your company too.
8. Post-Hiring: Failing to Retain ML/AI Talent
Hiring is only half the battle. Retention is just as critical—and often just as difficult. Machine learning engineers are constantly courted by recruiters, especially if they are on LinkedIn or publish on Medium or GitHub.
To keep top talent engaged:
- Offer continuous learning budgets or courses
- Encourage participation in conferences and hackathons
- Provide challenging work—not just endless bug fixes
Retention-friendly environments make tech companies stand out not just for hiring, but for long-term innovation.
Conclusion: The Talent Is Out There—If You Know Where (and How) to Look
Hiring machine learning and AI engineers may seem like a steep hill to climb, especially when you are competing with big names and facing a tight talent market. But the good news? It’s not a lost cause. With clearer job expectations, better outreach, and a willingness to explore remote and non-traditional talent pools, companies can significantly increase their chances of finding the right fit.
It’s not just about filling a role—it’s about finding a thinker, a builder, and a problem-solver who can help your business innovate faster and smarter. Whether you are a growing startup or an established tech company, the right AI or ML hire can shape the direction of your products—and your success.
Several organizations also overlook platform-specific roles. If you are planning to scale your content-heavy product, you may need to look for CMS developers for hire who can build reliable, manageable backend systems tailored for growth.
So take a fresh look at your hiring playbook. Simplify the process, stay flexible, and focus on skill and potential over titles and checkboxes. The future of your AI initiatives might just be waiting in your inbox.
Luys Parker