Why AI/ML Roles Are So Hard To Fill In The U.S.
- Quentin Sebastian
- 2 days ago
- 4 min read
— and why even AI recruitment agencies are struggling to keep up
Let’s not pretend. The U.S. AI job market is a battleground. Employers are circling the same scarce pool of elite talent. Salaries are ballooning. And the gap between academic training and real-world AI/ML deployment keeps widening.
Even the best AI recruitment agencies are feeling the strain. Sourcing top-tier Machine Learning Engineers, AI Research Scientists, or NLP experts isn’t just a challenge — it’s a high-stakes treasure hunt with global bidders.
Here’s why the struggle is so acute.
The Five AI/ML roles everyone wants — and few can fill
1. Machine Learning Engineer
The unicorn with code, math, and product sense
Why they’re rare:
Requires deep knowledge in ML algorithms, software engineering, and cloud infrastructure.
Needs practical deployment experience — not just academic flair.
U.S. challenge snapshot:
32% job posting growth from 2021–2022 (Dice Tech Jobs Report 2023).
Median salary: $131,001 (Glassdoor, 2023); top earners hit $180K+ in San Francisco.
42% of U.S. AI jobs concentrated in California alone (Stanford AI Index 2023).
The effect: If you’re not Google, you’re probably losing the bidding war.
2. AI Research Scientist
The academic-turned-innovator who feeds the future of AI
Why they’re rare:
Requires a PhD and a proven research record.
Needs to bridge cutting-edge theory with real-world impact.
U.S. challenge snapshot:
Only 1,841 computer science PhDs awarded in the U.S. in 2021 (NSF).
65% of AI PhDs go straight to industry, mostly to top tech firms (Stanford AI Index).
Median total comp at big firms: $250,000+ (Levels.fyi, 2023).
The effect: Startups and mid-size firms? Often priced out or ignored.
3. Data Scientist (AI-Specialized)
The analyst who also speaks ML fluently
Why they’re rare:
Must blend advanced ML skills with domain expertise and business intuition.
Too many are great at stats or Python — few are great at both and applying them.
U.S. challenge snapshot:
36% projected growth from 2021 to 2031 — way above average (BLS).
Median salary: $125,000+; AI specialists often earn more (Glassdoor, 2023).
40% of roles in just two states — California and New York (LinkedIn Jobs on the Rise, 2022).
The effect: Talent hoarding in coastal cities leaves a vacuum everywhere else.
4. Computer Vision Engineer
The eye behind AI — making machines see and interpret
Why they’re rare:
Requires deep learning and image processing expertise.
Often tied to highly specialized sectors: autonomous vehicles, medical imaging, defense.
U.S. challenge snapshot:
30% of U.S. AI patents relate to computer vision (CBRE 2022).
Median salary: $136,000; top end breaks $200K (Glassdoor, 2023).
Small talent pool: only ~2,000 AI-focused master’s grads annually (CRA Taulbee Survey 2021).
The effect: You're fishing in a tiny lake with too many rods in the water.
5. NLP Engineer
The language whisperer translating between humans and machines
Why they’re rare:
Must master ever-evolving models — transformers, LLMs, embeddings.
Few engineers are fluent in both the science and the nuance of language.
U.S. challenge snapshot:
25% increase in NLP job postings from 2021 to 2022 (Indeed Hiring Lab, 2023).
Median total comp at top firms: $180,000+ (Levels.fyi, 2023).
NLP dominates research, but workforce training lags (Stanford AI Index 2023).
The effect: Everyone’s racing toward LLM adoption, but there’s barely anyone to hire who gets it.
The bigger picture — Why the shortage persists
These roles don’t exist in isolation. They’re affected by larger structural forces shaping the U.S. AI talent landscape.
Limited Talent Supply
U.S. universities graduate only ~2,000–3,000 AI-specialized engineers annually (NSF, CRA).
Against that? Tens of thousands of open roles (BLS, Dice).
There’s simply not enough throughput from academia to meet demand.
Salary Inflation
Mid-level ML roles start at $130,000+.
Research and vision/NLP roles quickly leap to $200,000–$250,000 or more.
Smaller firms can’t compete — even with equity in the mix.
High demand plus low supply equals ballooning compensation packages.
Geographic Concentration
45% of U.S. tech jobs sit in San Francisco, Seattle, and NYC (CBRE 2022).
That leaves large parts of the country scrambling for leftovers.
Yes, 20% of tech jobs are now remote (LinkedIn 2023), but it doesn’t fix everything.
Remote access doesn’t equal remote interest — or retention.
Skill Mismatch
AI is evolving faster than universities and online courses can keep up.
LLMs, multimodal models, fine-tuning — most professionals are still catching up.
Employers want engineers ready now, not 6-month ramp-ups.
The frontier moves fast — and most candidates are chasing it, not leading it.
What AI Recruitment Agencies Can — and Can’t — Do
Let’s be honest. Even the most connected AI recruitment agency can’t invent talent.
But the right ones can:
Curate faster: Cut through noise and find the 1% who are both qualified and available.
Think globally: Tap international talent pools when U.S. supply stalls.
Guide compensation: Help clients avoid lowball offers that tank deals.
Vet technically: Ensure engineers can do more than just talk the talk.
However, no recruiter can change this core truth:
AI recruitment isn’t hard because recruiters are doing it wrong — it’s hard because the system can’t produce talent at the pace of its own ambition.
So, What Now?
Companies need to rethink how they compete in this warped market.
That means:
Investing in internal training for promising hires.
Reevaluating location strategies — regional universities can be unexpected goldmines.
Opening up to contract or project-based work while full-time roles stay unfilled.
Partnering with specialized AI recruitment agencies that actually understand what’s under the hood.
The AI talent gap isn’t going away. But the companies that adapt first — who get creative, flexible, and real — will be the ones who keep building while others keep hunting.
And in this space, execution always beats intention.
Need help navigating the talent chaos?
It might be time to rethink who’s sourcing your next AI hire.
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