Why Hiring an AI Engineer Is So Hard

Hiring an AI Engineer today can feel like searching for a unicorn. Everyone wants professionals who can build AI-powered products end-to-end, but few candidates check all the boxes. For context, when we say AI Engineer, we’re talking about the role described by Latent Space as a full-stack builder who leverages foundation models and ships intelligent applications, not just an academic machine learning researcher. These are the engineers turning AI advances (like GPT-4 or other foundation models) into real products, often without a PhD in sight1. And yet, hiring managers tasked with finding such talent are hitting a wall. Why is it so difficult to hire AI Engineers, and what can you do about it? Let’s break down the challenges.

An Overheated Talent Market

If you’re struggling to find AI Engineers, you’re not alone. The demand far outstrips the supply. The World Economic Forum projects 97 million new AI and related roles by 2025, yet there simply aren’t enough qualified people to fill them2. LinkedIn data shows a 74% year-over-year increase in AI-related job postings, signaling a hiring boom3. This is truly an AI talent gold rush – and every company is panning in the same river.

The result? Fierce competition. Tech giants and AI labs have already scooped up much of the world’s top AI talent. Microsoft, Google, OpenAI, Meta – they’ve cornered the market on AI researchers and experts, often offering eye-popping salaries to do so45. One analysis counted only about 5,000 true AI research scientists worldwide, versus 50 million software developers, and the big players have locked in most of those researchers6. For the rest of the industry, that means trying to hire the next tier of “AI Engineers” who can build on top of the big labs’ models – and everyone wants the few who are really good at it.

Competition isn’t just abstract; it’s driving real corporate behavior. There are reports of companies literally reorganizing themselves to chase AI talent. In mid-2024, Intuit announced 1,800 layoffs specifically to free up budget to hire for AI roles7. Think about that: they cut other staff to make room for AI specialists. This kind of musical chairs is becoming common. And once you do hire an AI Engineer, you might immediately have to fend off poachers. An internal Microsoft memo revealed managers now flag “critical AI talent” and offer retention bonuses, explicitly to prevent rivals from luring them away89. In short, the AI talent market is red-hot, and hiring managers are feeling the heat.

The Skill Mismatch Dilemma

High demand is only part of the story. Another big headache is skill mismatch – the gap between what employers think they need and what candidates actually offer. The AI field is evolving so fast that it’s causing confusion about required skills. According to a 2023 Coursera report, 68% of hiring managers struggle to even define the specific skills needed for AI roles, which leads to mismatched hires and frustration on both sides10. Job descriptions end up packed with every buzzword under the sun – and then the people who show up have backgrounds that don’t fit the actual work.

Consider the “AI Engineer” role itself. It sits at the intersection of software engineering and AI research, which is precisely why it’s tricky to pin down. Many hiring managers default to treating it like a traditional ML (machine learning) engineer or Data Scientist position, asking for PhD-level knowledge of models, tons of math, and years of data pipeline experience. But the reality is often different. As one analysis bluntly put it, “none of the highly effective AI Engineers… have done the equivalent of the Andrew Ng Coursera courses, nor do they know PyTorch, nor do they know the difference between a data lake or data warehouse.”11 In other words, the best AI Engineers aren’t necessarily academic ML researchers or big data gurus – they’re skilled hackers who understand AI capabilities and how to plug them into products. They might be experts at prompt engineering or integrating an API into a user-facing app. So when a corporate job listing insists on a laundry list of ML theory and low-level model training experience, it’s no wonder the actual AI builders don’t check those boxes.

This lack of alignment goes both ways. Candidates with traditional ML research backgrounds may lack the full-stack software skills or product mindset that modern AI engineering roles demand. Conversely, great software engineers might have the engineering chops but lack insight into AI model behavior. The outcome? Unfilled roles and failed projects. Harvard Business Review noted that 42% of AI projects fail due to a mismatch between the team’s skills and the project needs12. Companies set out to do an AI project with the wrong mix of people – say, all researchers and no product engineers, or vice versa – and they can’t execute. The “skill mismatch” isn’t just an inconvenience; it’s actively derailing AI initiatives.

Sticker Shock: Sky-High Compensation

Let’s talk about money. Because if the skill gap doesn’t scare you off, the salary demands might. AI Engineers are expensive – period. And they know it. By some estimates, the average machine learning engineer salary in the U.S. is around $160,000 per year13, and that’s an average across all levels. In hot markets like the Bay Area or New York, a solid AI Engineer will easily command six figures, often well into the mid-to-high six figures when you factor in experience and company size. In fact, a 2023 Deloitte survey found 62% of companies cite “high compensation demands” as a major barrier to hiring AI talent14. It’s not that you can’t find candidates – it’s that you can’t afford them if you’re not a tech giant or well-funded startup.

Just how high can it go? Top-tier AI engineers and researchers at leading firms are landing astronomical pay packages. OpenAI (creators of ChatGPT) reportedly paid a median of $900,000 in total compensation for its engineers15. Even “prompt engineers” – a job that barely existed two years ago – have been reported earning around $300,000 a year at startups like Anthropic16. These are extreme cases, but they set the tone. Even outside the rarefied air of AI labs, salaries for AI-focused roles are rising faster than for regular software jobs. In 2024, entry-level AI engineers were earning about 8.6% more than their non-AI software engineer counterparts, and senior AI engineers made about 10-12% more than seniors in standard software roles17. Companies are paying a premium for AI skills at every level.

_Median annual AI engineer salaries in top U.S. tech hubs (Levels.fyi, Q1 2024). The San Francisco Bay Area leads with a median of over $318,000

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It’s not just base salary, either. The competition leads to bidding wars with hefty stock grants, bonuses, and other perks. Multiple offers are common. If your compensation package isn’t compelling, that sought-after candidate will jump to a company that’s offering more – or, increasingly, they’ll start their own AI-driven venture. Hiring managers often face “sticker shock” when they realize that filling an AI role might mean ponying up 30-50% more than a similar non-AI role would cost. This can strain budgets and internal pay equity. But in this market, trying to cheap out on an AI hire is a recipe for an unfilled position – or a quickly departed employee.

Tech Moves Fast, and Job Posts Can’t Keep Up

Another challenge is the breakneck pace of change in AI technology and tooling. The ground is shifting under our feet every month, and that makes defining roles – and finding people with the “right” experience – incredibly hard. The state of the art in AI is a moving target. New frameworks, libraries, and techniques emerge seemingly overnight. Just in the past couple of years, we’ve seen the rise of whole new paradigms: transformers, diffusion models, large language model APIs, prompt engineering, retrieval augmentation, AI agents – the list goes on. Keeping up with this deluge is a full-time job in itself18.

For hiring managers, this means yesterday’s job description might already be outdated. It’s common to see job requirements asking for experience with a specific framework or model – only for that tech to become obsolete or superseded six months later. For example, a few years ago a posting might have sought someone with TensorFlow expertise; today the hot keyword might be PyTorch, and tomorrow it could be something like JAX or a new high-level API for foundation models. Tools like LangChain, Hugging Face, Ray, Docker, Kubernetes, Pinecone, etc., are constantly evolving the AI tech stack. Do you need someone who’s used a particular one, or just someone adaptable to whatever comes next? Many hiring managers aren’t sure, and so they throw everything into the wish list, hoping the right candidate ticks all the boxes.

The rapid evolution also means candidates’ experience can be all over the map. One candidate might have spent the last year building LLM-powered chatbots using API X and vector database Y. Another might have been heads-down fine-tuning custom models on an internal cluster. Their resumes look totally different, yet both call themselves “AI Engineers.” If your team’s needs pivot (as they likely will in this space), you might find that someone who was a perfect fit a year ago now needs significant upskilling to work with the latest tools or model APIs. This is nobody’s fault per se – it’s the nature of a nascent, fast-changing field. But it complicates hiring: do you hire for the tools you’re using today, or the general ability to learn the tools of tomorrow? Most would prefer the latter, but assessing that isn’t straightforward.

The pace of change even affects how candidates value opportunities. The best AI Engineers love to learn new things – they have to, or they’d fall behind. If they sense that joining your company means being stuck on old tech or not getting exposure to the latest developments, they’ll likely pass, no matter the salary. In that sense, companies have to sell not just what they are doing now, but that they’ll enable the engineer to grow and keep up with the field. It’s a lot to promise, and it requires the company itself to stay adaptable.

A Role in Flux: AI Engineer or Something Else?

Part of the hiring challenge is that “AI Engineer” is a moving target of a job title. It’s so new that everyone has a slightly different definition. Some companies treat “AI Engineer” as a rebranded Machine Learning Engineer. Others see it as a Product-focused Software Engineer who is savvy in AI. Others might expect it to be like a Data Scientist who also knows how to deploy models. This confusion leads to muddled expectations in hiring.

Even within the industry, the terminology is debated. The role is evolving as we speak. Shawn “Swyx” Wang, who helped popularize the term, describes an AI Engineer as distinct from an ML researcher, someone who focuses on applying AI models (often via API or fine-tuning) to solve product problems quickly19. It’s more about integration and iteration than inventing new algorithms. But not everyone got that memo. As the Latent Space essay noted, many people still assume AI Engineering is basically the same as traditional ML or Data Engineering20 – hence those job posts demanding advanced ML credentials. On the other hand, some job seekers label themselves “AI Engineers” because they took a few deep learning courses, when they might not have the end-to-end software skills needed. The title hasn’t stabilized yet; it’s an emergent role that’s still being defined on the fly.

For hiring managers (especially non-technical ones), this is a minefield. You might get pressure to “hire an AI person” without clarity on whether that means a data scientist, an ML engineer, a prompt engineer, or some hybrid. I’ve seen cases where a company posted for an “AI Engineer” and got mostly PhD researchers applying, when what they really needed was a good software engineer who’s handy with the OpenAI API and can ship a feature to production. Conversely, I’ve seen data science teams rebrand themselves as “AI Engineering” to appear more cutting-edge, even if the work hadn’t changed. This lack of role definition can lead to misalignment in the hiring process – the interview panel might not even agree on what they’re looking for!

The AI Engineer role will likely crystallize over time (it’s already far more common in 2025 than it was in 2021). But for now, hiring managers must be extra diligent in defining the role for their context. Are you looking for someone to build new ML models? Or someone to apply existing models in a product? Do they need strong frontend/back-end skills, or just enough to integrate a model output? How much weight on data engineering or MLOps? Without clear answers to these questions, you risk hiring the wrong “type” of AI professional or scaring off the right ones. Clarity is key – and it’s in short supply industry-wide.

The “Unicorn” Candidate Myth

Maybe you’ve done the hard work of figuring out what kind of AI Engineer you really need. Now comes a classic pitfall: writing an unrealistic job description that seeks an all-in-one superhero. It’s tempting: given how interdisciplinary AI projects can be, why not find that one perfect individual who can do everything? Someone who can wrangle data, train models, write production code, design experiments, tune infrastructure, and even talk to customers. Spoiler alert: that person doesn’t exist, at least not at the level of proficiency you’d ideally want. By trying to hire a unicorn, you likely scare off strong candidates who see the laundry list and self-select out, and whoever you do hire might be set up to fail.

The tech industry has been down this road before. In the early days of “data science,” companies expected one hire to handle data engineering, analytics, machine learning, and business strategy all at once – the mythical “full-stack data scientist.” It rarely worked out. As one veteran noted, many early data scientists quickly realized the company expected them to deliver end-to-end AI solutions solo, far beyond just building models21. Unsurprisingly, “too many failed”, and a lot of AI projects never made it to production due to these unrealistic expectations22. The lesson: trying to find a single person to do the job of what should be a team is a recipe for disappointment.

Yet in the AI Engineering context, the unicorn hunt continues. You see job postings that require expertise in Python, PyTorch/TensorFlow, distributed systems, Kubernetes, prompt engineering, UX design, data science, and oh, a PhD for good measure – all for a mid-level role. This is overkill. It usually signals that the company hasn’t figured out how to structure their AI efforts, so they hope one magical hire will figure it out for them. For the candidate, it’s a red flag: it suggests they’ll be alone, without support or complementary team skills, trying to juggle everything.

Hiring managers need to be realistic about what one person can do. AI Engineers are not unicorns, and the best ones typically specialize in being very good at a few things and merely competent in others. If you truly need an array of disparate skills, consider hiring multiple roles – for example, an AI engineer paired with a data engineer, or an ML engineer paired with a product engineer – rather than expecting one individual to be a master of all trades. Remember, even if by some miracle you find that “unicorn,” they might burn out or leave if you ask them to carry an entire AI initiative on their back. Keep expectations grounded in reality.

Retention: Keeping AI Talent Is Its Own Battle

Let’s suppose you succeed in hiring a great AI Engineer. The fight isn’t over – now you have to keep them. In a field as hot and fast-moving as AI, retention is a huge challenge. These professionals are bombarded with recruiting calls and LinkedIn messages daily. Competitors will try to lure them away with higher offers or promises to work on sexier problems. It’s truly a seller’s market for top AI talent, and that means retention requires special attention.

First, there’s the straightforward issue of poaching. We touched on this with Microsoft’s internal efforts to identify “can’t lose” AI people and throw money at them to stay23. When companies like Microsoft, Google, Meta, or a well-funded startup are determined to hire AI expertise, they often won’t take no for an answer. They can make life-changing offers (sometimes including multi-million dollar stock grants24) that are hard to refuse. If you’re a smaller player or can’t match on cash, you need other ways to make your engineers stick around (more on that in a moment).

Beyond money, AI engineers value learning and impact. The field changes so quickly that stagnation is a death knell to their career. If your AI Engineer feels like they’re not growing or the company isn’t keeping up with new developments, their eyes will start to wander. This is where many organizations falter – they hire a great person but then shackle them to maintenance or mundane tasks, or they underinvest in AI infrastructure, causing the engineer constant frustration. In a high-demand environment, talented folks have little patience for organizations that don’t enable their success. They will leave for places where they can ship things faster, learn new skills, or have a bigger say in product direction.

Retention can also be hurt by the “lone wolf” problem. If you hire one AI specialist and tuck them into a corner of a team that doesn’t understand their work, that person can feel isolated. Remember that Slack channel #discuss-ai that every startup suddenly had? The Latent Space essay predicted those would turn into formal AI teams25. People doing cutting-edge AI work want peers and a community, not to be the only one of their kind in an org. If they’re the sole AI engineer fighting to get buy-in or resources, burnout and attrition aren’t far behind.

So, retention comes down to treating these folks as the highly prized talent they are. Boston Consulting Group bluntly noted that attracting and holding onto AI talent is “not business as usual” – you have to offer a compelling value proposition and work environment tailored to what these engineers want26. Companies that succeed at retaining AI Engineers give them a clear growth path, interesting problems to solve, a say in strategy, and yes, competitive rewards. It’s about convincing them that they have a long-term future with you, not just a stepping stone.

Given all these challenges – scarce talent, skill mismatches, high costs, fast-changing requirements, fuzzy role definitions, unicorn fantasies, and retention risks – it might feel daunting to hire for an AI Engineer role. It is daunting. But it’s not impossible. Companies are finding ways to adapt. In fact, the very scarcity is driving some creativity and clarity in hiring.

Hiring Smarter: How to Attract and Keep AI Engineers

So what’s a hiring manager to do? You can’t change the market, but you can change your approach. Here are some actionable strategies to improve your odds of attracting, assessing, and retaining AI engineering talent:

• Define the Role (and Projects) Clearly: Before you even write the job description, nail down what you actually need for your business. Is it someone to productionize models via an API, or to build new models from scratch, or to optimize user flows with AI? Be specific about the scope. A clear, realistic role not only attracts the right people but also shows candidates that your company “gets it.” Vague or bloated postings will turn off savvy candidates. Remember, focus on skills and impact over credentials – for example, ability to prototype and integrate an AI feature is more important than a PhD title27.

• Don’t Chase Unicorns – Build a Team: Resist the urge to cram every possible requirement into one role. Prioritize the must-have skills that align with the role you defined, and be willing to let less critical ones go (or plan to train for them). If you have a broad need, consider hiring two complementary people (say, one more research-oriented, one more product-oriented) instead of a mythical all-rounder. As an example, some successful AI teams follow a model of 4 AI Engineers to 1 ML Researcher28 – recognizing that product-focused engineers are a distinct need alongside a smaller number of deep ML experts. Structure your hiring accordingly.

• Move Fast and Be Competitive (within Reason): In this market, a slow, bureaucratic hiring process will cost you good candidates. Streamline your interview loops and be ready to make an offer quickly if you find a fit. When it comes to compensation, do your homework on market rates – you likely will need to offer at the high end of your range to land an AI Engineer. If budget is a constraint, think about equity, bonuses, or other perks (flexible work, time for personal projects, etc.) that can sweeten the deal. Also be open to remote or global candidates if local supply is dry – great AI talent exists worldwide, and remote work is common in this field.

• Assess for Real-World Skills: Traditional interviews (algorithm puzzles or theoretical questions) might not uncover the skills that make an AI Engineer effective. Incorporate practical evaluations: ask candidates to walk through how they’d build a simple AI-powered feature, have them read and critique an AI model’s output, or do a short take-home project integrating an AI API. Look for their ability to translate AI capabilities into product solutions. Also probe their learning mindset – how do they keep up with new tools and research? Since the tech moves fast, a great candidate is one who can learn on the fly and adapt.

• Sell the Opportunity (Beyond Salary): Top candidates often have multiple options, so you need to articulate why they should join your company. Emphasize the interesting challenges they’ll tackle, the autonomy they’ll have, the team culture, and the impact they can make on real users. If you’re not a household-name company, highlight any unique assets you have – perhaps proprietary data, a strong community, or a mission that matters. Engineers motivated by impact and growth will respond to a story about what they can accomplish and learn with you, especially if you can show you have a plan for AI (and they won’t be alone fighting for resources).

• Invest in Retention from Day 1: Retention starts with setting up your hire for success. Ensure they have the tools and support they need – maybe that means budgeting for cloud GPU credits, or giving them an ML ops buddy to deploy models, or just a welcoming team that values their expertise. Create a path for continuous learning: encourage them to attend conferences, spend a fraction of their time on R&D or hackathons, and stay sharp. Pair them with peers or mentors (you could even start an internal AI guild or chapter) so they feel part of a community. And of course, recognize and reward their contributions. If they launch a successful AI feature, celebrate it and make sure leadership acknowledges it. These things go a long way in making an AI Engineer feel valued and excited to stay. As one consulting study found, companies need a compelling environment to “attract—and hold onto—these highly prized recruits.”29 If you don’t provide that, rest assured someone else will.

Hiring AI Engineers is certainly challenging, but it’s not hopeless. By understanding the market realities and adjusting your strategy, you can improve your chances of finding and keeping the right people. In the end, remember that an AI Engineer isn’t magic – they’re software professionals who thrive on solving problems with the latest tools. If you offer them a meaningful problem, a supportive team, and the resources to grow, you might be surprised at how much success you can find, even in this competitive landscape. The companies that win in the AI era will be those that learn to navigate these talent challenges with clarity, honesty, and a bit of creativity. Good luck out there – in a world of talent wars and unicorn myths, a little clear thinking goes a long way.