The seven-figure compensation packages you see splashed across tech Twitter? They're not for you—at least not yet. If you're a mid-career AI engineer with 3-8 years of experience, those eye-popping numbers represent the top 5% of Staff and Principal roles at major tech companies. For the rest of us in the middle band, the real opportunity often lies elsewhere.

After analyzing compensation data from Levels.fyi, talking to a dozen engineers who recently switched jobs, and running the numbers on dozens of offer letters, I've come to a counterintuitive conclusion: for most mid-career AI engineers in 2025, late-stage AI startups and well-funded remote companies often deliver better total value than equivalent FAANG positions.

Here's why the math works out that way—and how to evaluate your next offer accordingly.

What FAANG Actually Pays (When You're Not Staff+)

Let's start with reality. FAANG companies still pay extremely well, but the compensation distribution is top-heavy. According to Levels.fyi data from mid-2024, mid-level ML/AI engineers (L4/L5 equivalent) typically see total compensation in the $250,000-$400,000 range depending on the specific company and performance. Staff and Principal engineers, meanwhile, frequently break $600,000 and can exceed $1 million.

That's a massive gap—and it matters for your decision-making.

Base salaries tell a different immediate story. Glassdoor's 2024 data shows U.S. Machine Learning Engineer base pay clustering around $130,000-$150,000 for mid-career roles. The rest comes from RSUs vesting over four years. If you need cash flow now—for a mortgage, family expenses, or simply peace of mind—those paper valuations and distant vesting schedules don't help much.

Sarah Chen, a former Google ML engineer who recently joined a Series C startup, put it bluntly: "My Google offer was $320k total comp on paper, but only $145k was guaranteed cash that first year. When I had a baby on the way and student loans, that mattered more than RSUs vesting in year three."

Why Late-Stage Startups Often Win the Mid-Career Dollar

Late-stage AI startups structure compensation differently. They typically offer lower base salaries but compensate with larger equity grants and meaningful signing bonuses that put cash in your pocket immediately.

Let's run a realistic comparison:

FAANG L5 offer:

Base: $160,000
RSUs: $200,000 over 4 years ($50k/year average)
Signing bonus: $25,000 (one-time)
Year 1 cash: $185,000
4-year total: ~$390,000

Series C AI startup offer:

Base: $140,000
Sign-on: $50,000
Equity: 0.15% at $600M valuation = $900,000 paper value
Year 1 cash: $190,000
4-year value (conservative 0.5× exit multiple): ~$450,000
4-year value (moderate 1× exit multiple): ~$640,000

The startup offer delivers more immediate cash and—if you're willing to accept liquidity risk—potentially higher upside. Even under conservative scenarios, the expected value can exceed FAANG's mid-band guarantee.

Marcus Rodriguez, who left Amazon ML for a generative AI startup in 2023, explained his calculation: "Amazon's offer was stable and predictable. But when I modeled the startup equity at even a 0.5× discount to the last 409A valuation, it beat Amazon's total comp. The $40k signing bonus sealed it—I could pay off debt and still have upside."

The GenAI Premium: Where Specialization Actually Pays

If you have hands-on experience with LLMs, RLHF, or production GenAI systems, you should be negotiating for more—significantly more.

LinkedIn's 2023 Emerging Jobs Report documented salary premiums of 20-40% for LLM specialists compared to traditional ML roles. I've seen this firsthand in the offer letters engineers have shared with me. Companies productizing LLMs—especially startups racing to ship customer-facing features—are paying these premiums in either elevated base salaries or dramatically larger equity packages.

Big Tech will pay top dollar for senior research scientists and infrastructure engineers working at massive scale. But for mid-level engineers with production LLM experience, the fastest path to higher compensation is often at startups that must ship differentiated LLM products now and can't afford to wait for internal FAANG transfers.

When I spoke with Priya Malhotra, an engineer specializing in RLHF pipelines, she described receiving three offers within a week—all 30%+ above her previous salary. "Once I could point to production RLHF work and safety tooling I'd built, recruiters stopped negotiating on base. They just said yes."

How 2023-24 Layoffs Shifted the Playing Field

The tech industry's 2023-24 correction changed the negotiating landscape in subtle but important ways.

Meta cut approximately 11,000 positions in late 2022 (announced November 2022), followed by another 10,000 in 2023. Google reduced headcount by about 12,000 in early 2023. Amazon, Microsoft, and others followed with their own cuts. These weren't just symbolic; they fundamentally altered how FAANG companies approach mid-level hiring.

The result: compressed mid-band offers and slower promotion velocity. Companies became more conservative with RSU grants for L4/L5 roles and raised the bar for advancement to Staff levels.

Meanwhile, late-stage startups noticed the talent suddenly available and adjusted their strategies. Several well-funded AI companies increased cash compensation, boosted signing bonuses, and improved equity terms specifically to win candidates who might have defaulted to FAANG in previous years.

This created a window—still open in 2025—where mid-career engineers with competing offers have meaningful leverage. But you have to know what to ask for.

Remote Work and Geographic Arbitrage: The Real Numbers

Remote-first AI companies are increasingly offering Bay Area-equivalent compensation for mid-to-senior roles, regardless of where you live. According to Levels.fyi community data from 2024, companies like Anthropic, Cohere, and several well-funded stealth-mode startups match or approach Bay Area base salaries for competitive hires.

But—and this is critical—not all companies do this. Many legacy tech firms still use location-based pay bands. If you're evaluating a remote offer, you need to explicitly ask whether compensation is location-adjusted.

David Park made this mistake once and learned from it. After accepting what seemed like a generous remote offer from a major tech company, he discovered the compensation was benchmarked to Austin rates, not San Francisco rates—a roughly 20% difference. "When I got my offer letter, the numbers were lower than we'd discussed. HR explained it was 'adjusted for remote.' I had to renegotiate from a weaker position."

If you live outside expensive metros and can secure unadjusted compensation, the arbitrage can be substantial. A $160k base salary goes a lot further in Denver or Austin than in San Francisco—and if you're getting Bay Area equity on top of it, you're effectively receiving a significant raise.

Converting Equity Into a Decision You Can Defend

Most engineers accept equity grants on faith. Don't.

Here's a straightforward framework I use to evaluate any equity component:

Five-Step Equity Evaluation

1. Catalog guaranteed cash

Base salary
Signing bonus
Annual bonus target
This is your non-negotiable floor—the worst-case scenario if equity becomes worthless

2. Value the equity at current market price

For public companies: use current stock price
For private companies: use latest 409A valuation or recent secondary trading prices
Calculate: [your shares] × [price per share] = paper value

3. Apply risk multipliers

Conservative (0.5×): Assume significant dilution, down round, or market correction
Base case (1×): Company performs as expected, IPO or acquisition at current valuation
Optimistic (3×): Strong growth, successful exit, favorable market conditions

4. Discount for liquidity timing

How many years until you can actually sell?
Series B/C: probably 2-4 years
Pre-IPO: maybe 1-2 years
Public company RSUs: immediate after vesting
Apply a time-value discount (I use 10% per year for illiquid equity)

5. Compare the range of outcomes

Calculate Year 1 cash, Year 3 total value (with equity at each multiplier), Year 4 total value
Compare across all offers you're considering
Pick the one that matches your risk tolerance and financial runway

Real Example (Anonymized)

Maya, a 4-year engineer, evaluated two offers:

FAANG Offer:

Year 1 cash: $170k
4-year total (guaranteed): $380k

Series C Startup:

Year 1 cash: $185k (higher base + signing)
4-year value at 0.5×: $405k
4-year value at 1×: $565k
4-year value at 3×: $1.1M

Maya had $30k in credit card debt and student loans. She chose the startup specifically because the higher Year 1 cash let her clear debt immediately, and even the conservative equity scenario beat FAANG. "The $50k signing bonus was the deciding factor. I could breathe again."

Compare that with James, an 8-year engineer with six months of savings and a paid-off mortgage. He took a lower-base, higher-equity offer at a late-stage startup because his financial runway allowed him to prioritize upside over immediate cash. "I already had my emergency fund. I could afford to bet on the 1× or 3× scenario."

Different people, different risk profiles, different optimal choices—but both used the same math.

How to Actually Negotiate (Not Theory—Tactics)

When you sit down with a recruiter or hiring manager, here's what actually works:

Ask for more guaranteed cash first. Most companies have more flexibility on base salary and signing bonuses than you'd expect, especially for mid-career engineers with competing offers. Say: "I'm excited about this role, but I need to be at [X] base to make the numbers work. Can you get there?"

Push for larger equity percentages, not just more paper value. Instead of accepting "we'll grant you $200k in options," ask: "What percentage of the company does that represent, and what's the fully diluted cap table?" This forces transparency about dilution and gives you negotiating leverage.

Demand 409A and secondary market clarity. For private companies, ask: "What's the current 409A valuation? When was the last round priced, and at what valuation? Are there any active secondary markets?" These questions signal you're sophisticated and serious.

Use your FAANG offer as leverage even if you prefer the startup. Recruiters know exactly what Google, Meta, and Amazon pay at each level. If you have a FAANG offer, share the numbers. Say: "Google's total comp is $380k. I prefer your company, but I need you to close the gap on guaranteed cash or improve the equity terms."

Negotiate vesting schedules, not just grant sizes. Standard is 4-year vesting with a 1-year cliff. But you can sometimes negotiate: shorter cliffs, accelerated vesting on performance milestones, or partial acceleration on acquisition. These terms can meaningfully increase expected value.

Get everything in writing before you accept. "We'll review equity at 12 months" or "we expect to IPO next year" means nothing unless it's in your offer letter. If it matters to your decision, it needs to be written down and signed.

Priya negotiated hard on her latest offer: "I had three competing offers. I told each company the exact total comp numbers from the other two. The startup I wanted matched the FAANG base and increased the equity percentage by 0.05%. That small change was worth $180k in paper value. I just had to ask."

Special Considerations for Visa Holders

If you're on an H-1B or require visa sponsorship, you have fewer but more important leverage points.

U.S. Department of Labor LCA disclosures from 2022-2024 show Machine Learning Engineer roles commonly filed at wage levels between $120,000-$180,000 depending on region and experience level. This is the legally required prevailing wage—and it's one of the few enforceable protections you have.

Critical steps:

•Confirm the LCA wage level in writing. Ask: "What wage level will you file for my LCA?" Level 2-3 is standard for mid-career engineers. Anything lower is a red flag.
•Ensure base salary meets or exceeds the LCA wage. Companies cannot pay you less than the wage stated on your LCA. Get this in writing before you accept.
•Understand equity restrictions. Some visa conditions limit your ability to exercise options or receive equity. Clarify this upfront with an immigration attorney, not just the company's HR team.
•Negotiate harder on signing bonuses and relocation. These are one-time payments that don't affect your ongoing LCA compliance, so companies often have more flexibility here.
•Get job title and responsibilities specified precisely. The job description filed with your visa must match your actual work. Vague titles or shifting responsibilities can create compliance issues later.

David, who negotiated three H-1B offers in 2024, emphasized: "The LCA wage became my negotiating floor. Once I confirmed they were filing at Level 3 ($155k prevailing wage in my area), I pushed for $165k base because I had competing offers. They agreed because it was still within the range they'd file."

What You Should Do This Week

If you're actively evaluating offers right now, here's your action plan:

Monday: Build a spreadsheet comparing all offers. Columns: Base, Signing Bonus, Year 1 Cash, Equity Paper Value, 409A/Stock Price, 4-Year Total at 0.5×/1×/3× multipliers, Liquidity Timeline.

Tuesday: For every private company offer, email asking for: current 409A valuation, date of last funding round, any active secondary markets, and details on option pool size and dilution expectations.

Wednesday: If you have competing offers, reach out to your top choice and say: "I'm deciding between multiple offers this week. Your company is my first choice, but I need help closing the gap on [base/equity/whatever]. Can we talk before I decide?"

Thursday: Run the numbers one more time. Which offer gives you the highest Year 1 cash (if that matters)? Which gives you the best 4-year expected value at your chosen risk multiplier? Which company's product/mission excites you most?

Friday: Make your decision. Accept the offer that wins on the metrics you actually care about—not the metrics someone told you to care about.

The Bottom Line

The AI engineering job market in 2025 rewards sophistication. The highest-paying offer on paper often isn't the best offer in reality. FAANG compensation is top-heavy; the massive packages go to Staff+ engineers, not mid-level ICs. For engineers with 3-8 years of experience, late-stage startups and well-funded remote companies frequently deliver better total value—especially if you have GenAI specialization.

But "better value" depends entirely on your financial situation, risk tolerance, and career timeline. Someone with six months of runway should optimize differently than someone with debt to clear. Someone chasing Staff promotion at a top-tier company should think differently than someone building toward an eventual founding role.

The only universal truth: don't sign equity on faith. Run the numbers. Understand the risk. Negotiate hard on the things you actually need.

And if a recruiter tells you "this is our standard offer and we don't negotiate"—they're lying. Everything is negotiable if you know what to ask for.

Sources and Further Reading

Levels.fyi - Compensation data for ML/AI roles across tech companies: levels.fyi/salaries
Glassdoor Salaries - Machine Learning Engineer base pay data: glassdoor.com/Salaries
LinkedIn Economic Graph - Emerging Jobs Report 2023: economicgraph.linkedin.com
DOL Foreign Labor Certification - H-1B prevailing wage data: foreignlaborcert.doleta.gov
Meta Newsroom - 2022-2023 restructuring announcements: about.fb.com/news
Alphabet Investor Relations - 2023 workforce updates: abc.xyz/investor

All compensation figures represent aggregated market data from publicly available sources and anonymized offer letters shared with the author. Individual offers will vary based on experience, location, specialization, and negotiation. This article provides frameworks for evaluation, not financial advice.