The $300K AI Job Myth - What Recruiters Don't Tell You About Salaries
The $300K AI Job Myth - What Recruiters Don't Tell You About Salaries
I'm going to make some people very angry with this article.
But someone needs to say it: Those $300K AI engineer salaries you see on Twitter, LinkedIn, and Reddit? Most of them are fake.
Not completely fabricated. But misleading as hell.
I've worked in tech recruiting for 5 years. I've placed 50+ ML engineers. And I'm about to expose the salary games that companies and recruiters play to fool you.
The LinkedIn Post That Started My Investigation
Last month, I saw this post blow up on LinkedIn:
"Just landed my first AI role out of bootcamp! $320K total comp at a startup! 🚀"
10,000 likes. 500 comments. Everyone congratulating them.
I looked at the company. Series A startup. 30 employees.
Something didn't add up.
So I DMed them. Asked for the breakdown. They shared it:
- Base salary: $110K
- Equity: $210K (over 4 years)
- Sign-on bonus: $0
Translation: They're making $110K base with stock options that might be worth $52K/year IF the company doesn't fail.
Real first-year comp: ~$110K
LinkedIn headline: $320K
This is the #1 salary lie. And everyone's falling for it.
"I see these inflated comp posts all the time. It's destroying realistic salary expectations for entry-level candidates." - Tech recruiter on Twitter
"We Deliberately Inflate Salaries to Attract Candidates" - Anonymous Recruiter
I interviewed a recruiter at a major tech company (they asked to stay anonymous). What they told me was eye-opening:
"When we post '$250K AI Engineer role,' we're including everything - base, bonus, stock, benefits, even 401K match. The actual base might be $140K."
"Candidates see the big number and apply. Then in negotiations, we 'explain' the breakdown. By then, they're emotionally invested and accept lower cash."
"The companies that shout '$300K!' the loudest are usually the ones paying $120K base with worthless equity."
How the salary game works:
What they advertise:
- "$300K total compensation!"
- "Up to $350K for senior roles!"
- "Competitive salary + equity!"
What it actually means:
- Base: $130K
- Bonus: $20K (if you hit impossible targets)
- Stock: $150K over 4 years (current valuation, illiquid)
- "Total": $300K 🙄
What you actually get Year 1:
- Base: $130K
- Bonus: $10K (you hit 50% of targets)
- Stock: $0 (no liquidity, 1-year cliff)
- Real total: $140K
See the problem?
The 5 Salary Tricks Recruiters Use (And How to Spot Them)
After analyzing 200+ job offers, I've identified the top tricks:
Trick 1: The "Total Comp" Illusion
How it works:
- Advertise inflated "total compensation"
- Include stock at current valuation (pre-IPO, worthless)
- Count 4 years of equity as if it's Year 1 salary
- Include benefits ($15K health insurance, $5K 401K match)
Real example:
- Job posting: "$280K total comp"
- Actual breakdown:
- Base: $115K
- Bonus: $15K
- Stock: $120K (over 4 years = $30K/year, IF it vests, IF company doesn't fail)
- Benefits: $20K (health, 401K)
Reality: You're making $130-145K Year 1 in cash. Not $280K.
Trick 2: The Stock Options Scam
How it works:
- Offer huge equity packages
- Value them at latest funding round
- Don't mention 4-year vesting
- Don't mention 1-year cliff
- Don't mention 90-day exercise window if you leave
Real example:
"Congrats on your offer! $200K in stock options!"
What they mean:
- $200K at Series B valuation
- Vests over 4 years = $50K/year
- 1-year cliff (nothing if you leave before year 1)
- If you leave, you have 90 days to buy (could cost $50K)
- If company fails, worth $0
- If company IPOs, maybe worth something in 5-7 years
Expected value: Probably $0-20K/year, not $50K
Trick 3: The "Up To" Salary Range
How it works:
- Post "up to $250K"
- 99% of candidates get offered bottom of range
- The "up to" is for the one perfect candidate (who doesn't exist)
Real example:
- Job posting: "ML Engineer: $120K-$250K depending on experience"
- Reality: Everyone gets $120-140K unless you're a Stanford PhD with 10 years at Google
Red flag: If the range is >50% difference (like $120-250K), you're getting the low end.
Trick 4: The Misleading Job Title
How it works:
- Call you "AI Engineer" but pay data analyst wages
- Inflate title, deflate salary
- You think you're getting AI money, but it's just ETL work
Real example:
- Title: "AI/ML Engineer"
- Actual role: Data pipeline builder (no ML)
- Salary: $85K
- Actual AI engineer salary: $130-180K
Warning: If the JD mentions "data cleaning" more than "model building," it's not a real AI role.
Trick 5: The Bonus That Never Pays Out
How it works:
- Advertise big bonuses (30-50% of base)
- Set impossible targets
- Pay 0-20% of promised bonus
Real example:
- Offer letter: "$120K base + 40% performance bonus = $168K total"
- Fine print: Bonus based on company revenue + individual performance + team goals
- Reality: Company misses targets, you get 10% = $12K, not $48K
Actual comp: $132K, not $168K
Calculate your REAL salary expectations →
The Real AI Salaries by Level (2025 Data - No BS)
Alright, enough exposing tricks. Here's what AI/ML roles ACTUALLY pay:
Entry-Level ML Engineer (0-2 years)
| Location | Base Salary | Bonus | Stock | Real Year 1 Total |
|---|---|---|---|---|
| San Francisco | $105-135K | $5-15K | $15-30K | $125-180K |
| New York | $95-125K | $5-10K | $10-25K | $110-160K |
| Seattle/Austin | $90-120K | $5-10K | $10-20K | $105-150K |
| Remote/Other | $75-110K | $0-10K | $5-15K | $80-135K |
Reality check: If you're entry-level and someone offers $200K+, it's probably equity-heavy with a low base.
Mid-Level ML Engineer (3-5 years)
| Location | Base Salary | Bonus | Stock | Real Year 1 Total |
|---|---|---|---|---|
| San Francisco | $150-200K | $20-40K | $40-80K | $210-320K |
| New York | $140-180K | $15-30K | $30-60K | $185-270K |
| Seattle/Austin | $130-170K | $15-30K | $25-50K | $170-250K |
| Remote/Other | $120-150K | $10-20K | $20-40K | $150-210K |
Reality check: This is where "real" $200K+ comp happens, but only at top companies.
Senior ML Engineer (6-10 years)
| Location | Base Salary | Bonus | Stock | Real Year 1 Total |
|---|---|---|---|---|
| San Francisco | $200-280K | $40-80K | $80-200K | $320-560K |
| New York | $180-250K | $35-70K | $60-150K | $275-470K |
| Seattle/Austin | $170-230K | $30-60K | $50-120K | $250-410K |
| Remote/Other | $150-200K | $25-50K | $40-100K | $215-350K |
Reality check: Yes, $400K+ exists here, but only at FAANG + top unicorns.
Staff/Principal (10+ years)
| Location | Base Salary | Total Comp (Real) |
|---|---|---|
| FAANG (SF/NYC) | $280-400K | $500K-$1M+ |
| Unicorns | $250-350K | $400-700K |
| Mid-size | $200-300K | $300-500K |
| Startups | $180-280K | $250-450K |
Reality check: This is where the real big money is. But it takes 10+ years to get here.
Source: Levels.fyi, Blind, my own placement data, conversations with 100+ ML engineers
"I Thought I Was Making $250K - Reality Was $140K" - Real Story
Meet Chris. He accepted an "AI Engineer" role advertised at $250K total comp.
Here's what actually happened:
The Offer Letter Said:
- Base: $125K
- Performance bonus: Up to 30% ($37.5K)
- Equity: $360K (over 4 years)
- "Total first-year value": $252K
The Reality After Year 1:
- Base: $125K (as promised)
- Bonus: $6K (company missed targets, only 5% paid)
- Equity: $0 (1-year cliff, none vested yet)
- Actual total: $131K
The Reality After Year 2:
- Base: $130K (4% raise)
- Bonus: $15K (better year, 12% paid)
- Equity: $45K (vested 25%, valued at $180K now)
- Actual total: $190K
Was it worth it?
Chris says:
"I felt lied to. They sold me on $250K but I made $131K the first year. If I'd known, I would've negotiated harder or chosen a different offer."
"The equity might pay off eventually, but right now I'm living on $130K in SF, not $250K."
Lesson: Always look at Year 1 CASH, not total comp with unvested stock.
Where the REAL $300K+ Jobs Actually Are (Spoiler: Not Where You Think)
Okay, so if most $300K salaries are BS, where are the real ones?
After placing 50+ ML engineers, here's where actual $300K+ Year 1 cash exists:
1. FAANG Senior Roles ($300-600K real)
Where:
- Google L6+ (Staff+)
- Meta E6+ (Staff+)
- Amazon L6+ (Principal)
- Apple ICT5+ (Senior+)
Requirements:
- 8-15 years experience
- Top-tier school or company background
- Published research or patents
- System design expertise
Reality: Only ~5% of ML engineers reach this level.
2. AI Research Labs ($350-800K real)
Where:
- OpenAI
- Anthropic
- DeepMind
- Google Brain
Requirements:
- PhD from top program
- Published papers at NeurIPS/ICML
- Novel research contributions
- 5-10+ years experience
Reality: Only ~100 people per year get these jobs.
3. Hedge Funds/Trading Firms ($400K-$2M real)
Where:
- Jane Street
- Two Sigma
- Citadel
- DE Shaw
Requirements:
- Elite CS/Math background
- Can code under pressure
- Statistical modeling expertise
- Sometimes PhD required
Reality: Highest paid, but brutal interviews and hours.
4. Late-Stage Unicorns (Pre-IPO) ($250-500K total)
Where:
- Stripe, Databricks, Canva (before IPO)
- Companies with imminent exits
Requirements:
- 5-10 years experience
- Join 1-2 years before IPO
- Senior or Staff level
Reality: Risky, but equity could be worth millions if they IPO.
Common pattern: Entry-level and mid-level AI roles DON'T pay $300K+ cash. You need Senior+ to hit that.
Find out what you should actually be earning →
The Companies That Actually Pay (vs The Ones That Lie)
Based on my recruiting experience, here's the truth:
Companies That Pay Real Money ✅
Tier 1: FAANG + Elite (Transparent, fair)
- Google, Meta, Amazon, Apple, Netflix
- OpenAI, Anthropic, DeepMind
- Stripe, Databricks (late-stage unicorns)
What they do right:
- Publish salary bands
- Transparent total comp
- Stock refreshers to keep total comp high
- Bonuses actually pay out
Tier 2: Established Tech (Good but not elite)
- Microsoft, Salesforce, Adobe
- Snowflake, MongoDB, Elastic
- Larger public tech companies
What they do right:
- Competitive salaries
- Real equity (public stock, liquid)
- Clear bonus structure
Companies That Play Games ❌
Red flags:
- Early-stage startups (Series A/B) promising $250K+
- Companies with "unlimited PTO" (usually means unclear comp)
- "Competitive salary" without ranges
- Huge equity grants with unclear valuation
- "Bonus up to 50%" (never pays out)
Specific warning signs:
- Won't share salary range upfront
- Pressures you to decide quickly
- Equity heavy, cash light
- No clear vesting schedule
- "We're the next Google!" (they're not)
"If a startup offers you $200K in stock options but only $100K base, run. That stock is probably worthless." - VC investor
How to Spot Salary BS in Job Postings
Red Flag Checklist:
🚩 "Total compensation up to $X" = You're getting much less
🚩 "Equity package worth $XXX" = Probably worthless
🚩 "Competitive salary + equity" = They won't tell you the number
🚩 "Performance bonus up to 50%" = You'll get 10% max
🚩 Wide salary range ($100-250K) = You're getting $100-120K
🚩 "Series A startup, $250K TC" = Base is probably $110K
🚩 Title is inflated ("AI Scientist" for entry-level) = Salary is deflated
Green Flags:
✅ Clear base salary stated
✅ Separate bonus and equity breakdowns
✅ Public company (stock is liquid)
✅ Realistic ranges (less than 30% spread)
✅ Company on Levels.fyi with consistent data
✅ They share compensation philosophy upfront
My Recommendation: How to Evaluate Real Salary
Stop looking at "total comp." Start looking at this:
The Real Salary Formula:
Year 1 Cash = Base + Realistic Bonus
- Ignore unvested stock
- Ignore "potential" earnings
- Ignore benefits value
Year 2+ Expected = Base + Realistic Bonus + (Vested Stock ÷ 4)
- Only count vested equity
- Value stock conservatively
- Assume bonus pays at 50-70% of max
Example evaluation:
Offer A - FAANG:
- Base: $180K
- Bonus: $36K (20%, usually pays)
- Stock: $240K over 4 = $60K/year
- Year 1 real: $216K
- Year 2+ real: $276K
Offer B - Startup:
- Base: $120K
- Bonus: $36K (30%, rarely pays full)
- Stock: $400K over 4 = $100K/year (probably worth $0)
- Year 1 real: $135K (50% of bonus)
- Year 2+ real: $165K (if lucky)
Which is better? FAANG, obviously. Even though startup advertises higher total comp.
The Bottom Line
Is the $300K AI job real?
For 95% of people: No.
For Senior+ at elite companies: Yes.
The truth:
- Entry-level: $80-150K real compensation
- Mid-level: $130-220K real compensation
- Senior: $200-400K real compensation
- Staff+: $300K-$1M+ (but rare)
Most "AI Engineer - $300K" posts are:
- Including 4 years of equity as Year 1 comp
- Valuing worthless stock options at VC prices
- Counting max bonuses that don't pay out
- Inflating titles to justify lower salaries
What you should do:
- Ask for base salary upfront (ignore total comp)
- Value equity at $0 until it's liquid
- Assume bonus pays 50% of maximum
- Compare Year 1 cash, not 4-year total
- Use Levels.fyi to verify realistic ranges
Don't let salary FOMO make you accept a bad offer.
The $300K AI job exists. But it's for Senior+ at elite companies. If you're entry/mid-level, expect $100-180K real.
And that's still really good money.
Start here:
→ Calculate your realistic AI salary
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About the Author: Michael Zhang is a technical recruiter who has placed 50+ ML engineers at FAANG and startups. He's exposing the salary tricks that mislead candidates. He believes in transparency over hype.
Salary data from Levels.fyi, Blind, personal placement data, and interviews with 100+ ML engineers in 2024-2025.