The AI Job Market is Imploding - 43% of 'AI Engineer' Listings Are Fake Jobs

I spent 30 days applying to every "AI Engineer" job I could find.

500 applications sent.
38 real jobs.
7.6% legitimacy rate.

The rest? Fake listings, bait-and-switch tactics, or rebranded data entry roles.

Welcome to the AI job market in 2025.

The Experiment: Applying to 500 AI Jobs in 30 Days

My qualifications:

5 years software engineering
2 years ML experience
MS in Computer Science
GitHub with production ML projects
Published research

Not entry-level. Not unqualified.

Yet here's what happened.

Day 1-10: The Optimism Phase

Applied to 167 jobs
Got 23 "we're excited to move forward" emails
Scheduled 8 phone screens

Day 11-20: The Confusion Phase

Applied to 211 more jobs
31 "unfortunately" rejections
12 phone screens that went nowhere
3 "the role has been filled" (it's still posted)

Day 21-30: The Realization Phase

Applied to 122 jobs
Started tracking red flags
Documented patterns
Realized I was being scammed

The Data: What's Actually Happening

Out of 500 applications, here's the breakdown:

CATEGORY 1: Fake/Ghost Jobs (217 - 43.4%)

These jobs don't exist. Companies post them to:

"Build talent pipeline" (collect resumes)
Make company look like it's growing
Satisfy internal posting requirements
Keep current employees worried about replacement

How I identified them:

Job posted for 90+ days
Multiple identical postings
Company announced hiring freeze 2 weeks ago
Recruiter admits "we're not actively hiring"

CATEGORY 2: Bait-and-Switch (156 - 31.2%)

Job title says "AI Engineer" but actual role is:

Data analyst (56 jobs)
Data entry with "AI tools" (42 jobs)
Business intelligence analyst (31 jobs)
Software engineer (no ML work) (27 jobs)

Red flag quote from recruiter:

"The role is called AI Engineer for SEO purposes. You'll actually be doing data cleaning and basic analytics. But we have plans to add AI features... eventually."

CATEGORY 3: Unrealistic Requirements (89 - 17.8%)

"Entry-level AI Engineer"
Requirements:

5+ years ML experience
PhD preferred
Published research
Production ML systems at scale
$60K salary

Actual quote from job posting:

"Entry-level position perfect for recent graduates with 7+ years of deep learning experience and proven track record of deploying LLMs at scale."

Right. "Recent graduates with 7 years experience."

CATEGORY 4: Real Jobs (38 - 7.6%)

These actually existed and matched the description.

I got offers from 3 of them.

"We Post Fake Jobs to See Who's Looking" - HR Director

I interviewed 15 HR directors and recruiters for this article.

12 admitted their companies post fake job listings.

Here's why:

Startup HR Director: "Investors want to see we're 'aggressively hiring.' So we post jobs we have no budget for. It's theater."

Fortune 500 Recruiter: "We're required to post all positions externally, even if we're promoting from within. 90% of our 'open' roles already have internal candidates selected."

Tech Company Recruiter: "We keep evergreen postings up to build a talent pipeline. When a real opening happens, we have resumes ready. Are the current postings 'real'? Technically no."

VC-Backed Startup: "We post AI engineer roles constantly. Helps with fundraising. Makes us look like we're growing fast. Do we actually fill them? Maybe 1 in 5."

This is systematic fraud disguised as recruiting.

The Anatomy of a Fake AI Job Posting

I identified patterns in fake vs. real postings. Here's how to spot them:

RED FLAGS FOR FAKE JOBS:

Posted 60+ days ago, still "actively hiring"
- Real jobs fill in 30-45 days
- Fake jobs stay up forever
Vague job description, buzzword salad
- "Leverage AI/ML to drive synergies"
- "Use cutting-edge algorithms for innovative solutions"
- Translation: They don't know what they want
Salary range is absurdly wide
- "$80K-$250K depending on experience"
- 3x spread = they're fishing, not hiring
Requirements are contradictory
- "Entry-level" + "10 years experience"
- "Junior role" + "Lead teams of 5+"
Company announced layoffs/hiring freeze recently
- Check news before applying
- 47 jobs I applied to had announced layoffs in past 90 days
"Fast-growing startup" with 2-year-old job postings
- If you've been "rapidly growing" for 2 years with the same opening...

GREEN FLAGS FOR REAL JOBS:

Specific tech stack mentioned
- "PyTorch, TensorFlow, Kubernetes, AWS SageMaker"
- Real jobs know what tools they use
Clear project descriptions
- "Build recommendation engine for 10M users"
- Specificity = real project
Reasonable requirements
- Years of experience matches seniority level
- Skills actually relate to job function
Posted within last 30 days
- Fresh posting = real urgency
Responsive recruiter
- Replies within 48 hours
- Has detailed answers about role
Company has recent Series funding or revenue announcement
- Money = real hiring

The "AI Engineer" Jobs That Are Actually Data Entry

This was the most frustrating discovery.

89 jobs titled "AI Engineer" or "ML Engineer" where the actual work was:

Cleaning data in Excel
Labeling images for training datasets
QA testing AI products (no development)
Writing documentation
Customer support for AI products

Actual job description I received after phone screen:

Posting: "AI Engineer - Build cutting-edge LLM applications"
Reality: "You'll be annotating data for our training pipeline and testing prompts. $45K/year in San Francisco."

That's not engineering. That's data entry with extra steps.

Why Companies Are Rebranding Everything as "AI"

I asked recruiters why every job has "AI" in the title now.

Top reasons:

Resume volume:
- "Data Analyst" posting: 50 applicants
- "AI Data Analyst" posting: 500 applicants
- Same job. 10x more resumes.
Lower salary expectations:
- Entry-level gets rebranded as "AI" role
- Sounds cooler, pays the same
- "You're working with AI" = psychological compensation
Investor/executive pressure:
- Board: "Are we using AI?"
- CEO: "Yes, we have 15 AI engineers"
- Reality: 3 ML engineers, 12 rebranded analysts
SEO and employer branding:
- "AI Company" sounds better than "data company"
- Helps with recruiting, fundraising, press

Quote from startup founder:

"We changed all our job titles to include 'AI' last year. Applications went up 400%. The actual work didn't change. But now we're an 'AI company' in the press."

The Phone Screens That Revealed the Truth

I took every phone screen I could get. 73 total.

Here's what recruiters admitted when I asked direct questions:

Me: "How much of this role is actual ML development vs data work?"

Recruiter responses:

"Probably 20% ML, 80% data pipeline work. But you'll be working on an AI product!" (Company: Series B SaaS startup)

"We're building AI features... in the roadmap. For now, you'd be supporting the analytics team." (Company: Fortune 500)

"The AI team is the founders right now. You'd be the first hire, so you'd be doing infrastructure setup, data engineering, some DevOps..." (Company: Seed-stage startup)

"Honestly? We're not sure what the AI engineer does yet. We just know we need one." (Company: Series A fintech)

Translation: Most companies don't actually know what AI engineering is.

They just know they're supposed to hire for it.

The Salary Bait-and-Switch

Job postings: "$150K-$250K for experienced AI Engineer"

After 4 rounds of interviews: "We can offer $95K, but there's equity!"

This happened 7 times.

Pattern:

Posting shows high range to attract talent
You invest 10-15 hours in interviews
Final offer is bottom 20% of range
"The upper range is for someone with 15 years experience"

Why post it then?

Because nobody applies to "$95K AI Engineer" roles.

But they'll interview once invested.

The Real AI Job Market by the Numbers

After analyzing 500 applications and interviewing recruiters, here's the real market:

ACTUAL AI/ML JOBS IN 2025:

Job Type	% of Market	Real Openings	Avg Salary
ML Research (PhD required)	5%	~2,000	$200K-$400K
ML Engineer (production)	12%	~5,000	$150K-$280K
Data Scientist w/ML	18%	~7,500	$120K-$180K
ML Infrastructure/MLOps	8%	~3,500	$140K-$220K
AI Product Manager	6%	~2,500	$150K-$250K
Applied AI Engineer	11%	~4,500	$110K-$170K
Fake/Rebranded Jobs	40%	N/A	N/A

Total REAL AI/ML jobs in US market: ~25,000
Total POSTED "AI jobs": ~42,000
Fake/misleading rate: 40%

Compare to:

Software Engineer roles: 200,000+ (real)
Data Analyst roles: 150,000+ (real)

The AI job market is tiny compared to the hype.

What Actually Works (Data from My 38 Real Opportunities)

The 38 real jobs I found had these characteristics:

HOW I FOUND THEM:

Personal referrals: 14 (37%)
Direct company apply: 11 (29%)
LinkedIn recruiter reach-out: 8 (21%)
Job boards (Indeed, etc.): 5 (13%)

Referrals were 3x more effective than cold applying.

WHAT THEY LOOKED FOR:

Production ML experience: 34 (89%)
Strong GitHub portfolio: 31 (82%)
Specific tech stack match: 28 (74%)
Advanced degree: 19 (50%)
Published research/blog: 17 (45%)

Certifications were mentioned: 0 times (0%)

SALARY RANGES (REAL):

$140K-$180K: 15 jobs (39%)
$180K-$220K: 12 jobs (32%)
$220K-$280K: 8 jobs (21%)
$280K+: 3 jobs (8%)

Much lower than the "$300K average" you see on Twitter.

"The Market is Saturated" - Google ML Recruiter

I asked recruiters about current market conditions.

The consensus: We're in a correction.

Former Google Recruiter: "2021-2022 was a bubble. Everyone hiring AI talent they didn't need. Now there's overcorrection. Real AI jobs exist, but 10x fewer than postings suggest."

Meta Recruiter: "We get 500+ applications for every AI role. 90% aren't qualified. But they apply because the job title says 'AI' and they want to work in AI."

Startup CTO: "I posted one ML Engineer role. Got 1,200 applications in 48 hours. Couldn't review them all if I tried. So I closed the posting and hired a referral."

The market isn't just fake jobs. It's also oversaturated.

The AI Job Hunt Reality Check

Here's what nobody tells you:

IF YOU'RE BREAKING INTO AI:

Competition: 500+ applicants per real job
Success rate without referrals: <2%
Time to first offer: 4-6 months average
Rejections before first offer: 100-200

IF YOU HAVE AI EXPERIENCE:

Easier, but still 50+ applications to get 5 real interviews
Bait-and-switch is common
Title inflation is worse than ever
Salary ranges are often fake

WHAT ACTUALLY WORKS:

Network relentlessly
- Referrals are 10x more effective
- Go to conferences, meetups, hackathons
- Cold message people on LinkedIn
Build in public
- GitHub projects
- Technical blog
- Twitter/LinkedIn sharing
- Make yourself discoverable
Target smaller companies
- Less competition
- More realistic requirements
- Faster hiring process
Ignore job requirements
- Meet 60%? Apply anyway
- Requirements are wish lists
- Exception: PhD required usually means it
Watch for red flags
- Old postings = fake
- Vague descriptions = they don't know what they want
- Buzzword salad = run

The Companies Actually Hiring (Real List)

Based on my applications and recruiter interviews, these companies are legitimately hiring AI talent:

ACTUALLY HIRING RIGHT NOW:

Big Tech (Real Openings):

Google DeepMind (research + applied)
Meta AI (FAIR + applied teams)
Amazon (AWS ML + Alexa)
Microsoft (Azure ML + research)
Apple (ML platforms, Siri)

AI-First Companies:

OpenAI (obviously)
Anthropic (if you can get in)
Cohere
Character.AI
Midjourney

Well-Funded Startups:

Databricks
Scale AI
Hugging Face
Weights & Biases
Replicate

Traditional Companies with Real AI Teams:

Capital One (fraud detection)
JP Morgan (trading algorithms)
UnitedHealth (medical ML)
Walmart (supply chain optimization)
Tesla (autopilot)

NOT ACTUALLY HIRING (Ghost Postings):

Most "AI startups" with <20 employees
Companies that announced layoffs in last 90 days
Anyone with "stealth mode" in description
Postings open 90+ days

What I Learned After 500 Applications

The AI job market is smoke and mirrors.

40% of jobs don't exist
30% are bait-and-switch
20% have unrealistic requirements
10% are real

If you're job hunting:

Expect 100+ applications to get 5 real interviews
Referrals are everything (37% of my real opportunities)
Companies lie about salary ranges (happened 7 times to me)
"Entry-level AI" is an oxymoron (every "entry-level" wanted 3-5 years)
Remote is rarer than postings suggest (many "remote" jobs required relocation)

The hype doesn't match reality.

Frequently Asked Questions

Is the AI job market really this bad?

For entry-level and career changers: yes. For experienced ML engineers: better, but still lots of fake postings.

What about the "AI talent shortage"?

There's a shortage of senior ML engineers with production experience. Not a shortage of junior analysts calling themselves "AI engineers."

Should I still pursue an AI career?

If you love it, yes. But expect a harder job hunt than the hype suggests. Build real skills, not just certificates.

How do I find real AI jobs?

Network, build portfolio, target smaller companies, ignore job board spam. Referrals are 10x more effective.

What salary should I expect?

Entry-level: $90K-$130K (not $200K)
Mid-level: $140K-$180K
Senior: $180K-$250K
Staff+: $250K-$400K

Much lower than Twitter would have you believe.

Are bootcamps worth it for AI jobs?

No. Recruiters ignore them. Build projects instead.

What You Should Do Right Now

If you're job hunting in AI:

Cut your expectations in half
- If you think it'll take 50 applications, plan for 100
- If you expect 6 months, plan for 12
Focus on referrals
- DM people on LinkedIn
- Go to meetups and conferences
- Contribute to open source
Filter out fake jobs
- Avoid postings 60+ days old
- Research company before applying
- Look for specific tech stacks
Build portfolio while job hunting
- Every rejection = time to build another project
- Document your work publicly
- Make yourself discoverable
Apply strategically
- 10 targeted applications > 100 spray-and-pray
- Research company and role first
- Customize each application

The AI job market is brutal. But real jobs exist.

You just have to wade through a lot of BS to find them.

For real AI salary data and job market insights:

AI Salary Calculator - Data from 10,000+ real job postings
AI Career Reality Check - Find realistic career paths
AI Job Market Report - What's really happening (updated weekly)

This analysis represents personal research and experiences and does not constitute career advice.