Startup Equity for Engineers: What Those Stock Options Actually Mean
By
Samara Garcia
•
Feb 23, 2026
You are an AI engineer with two offers on the table. One is FAANG with $260k, liquid RSUs, and certainty. The other is $190k plus 0.75 percent equity at a Series A AI startup. The decision hinges on one question: what is that equity actually worth?
Startup equity is ownership with real risk and real upside, yet many engineers are forced to guess how dilution, vesting, and strike price affect the outcome. Fonzi AI removes that guesswork by requiring salary and equity transparency upfront through structured Match Day hiring.
This article shows how to evaluate startup equity clearly and compare it to Big Tech compensation with confidence.
Key Takeaways
Startup equity represents ownership in a company, typically granted as stock options or restricted stock units (RSUs), but it only becomes real money at a liquidity event like an acquisition or IPO, and most startups never reach one.
A 0.5% equity stake at a 2025 Seed-stage AI startup valued at $20M could be worth $100,000 on paper today, but $5M if the company exits at $1B, or $0 if it fails. Understanding this range is critical.
Vesting schedules (typically 4 years with a 1-year cliff) mean you don’t own your equity immediately; leave before your cliff, and you walk away with nothing.
FAANG RSUs offer liquidity and predictable value, while startup equity trades certainty for potentially 10-100x upside, knowing your risk tolerance determines which makes sense for you.
Fonzi AI helps engineers compare equity offers across multiple startups by requiring companies to commit salary and equity ranges upfront, making Match Day a high-signal way to evaluate real compensation packages side by side.
Startup Equity Basics: What You’re Actually Getting
When a startup grants you equity, you’re receiving ownership interest in the company. But “ownership” takes different forms, and understanding the specifics determines whether your grant has real value or is essentially a lottery ticket.
Most startup equity for engineers arrives as stock options, the right to buy shares at a fixed price (the strike price) sometime in the future. If you receive 20,000 options out of 20,000,000 fully diluted shares, you have 0.1% ownership percentage. That percentage only matters if the company’s valuation grows and you can eventually sell those shares.
Here’s a concrete example of how vesting schedules work for an engineer joining a Seed-stage AI tools startup in mid-2025:
Grant: 40,000 options
Vesting period: 48 months (4 years)
Cliff: 12 months
Vesting after cliff: 25% (10,000 options) on month 12, then 1/36th of the remaining 30,000 monthly
This means if you leave at month 10, you get nothing. If you stay through month 12, you own the right to exercise 10,000 options. Every month after that, another 833 options vest until you’ve earned all 40,000.
Key terms you’ll encounter:
Grant date: When the company officially awards you options
Strike price (exercise price): The fixed price you pay per share to convert options into actual stock
Fair market value: The current assessed value per share, typically set by a 409A valuation
Liquidity event: An IPO, acquisition, or secondary sale where you can actually convert shares to cash
Cliff: The initial period (usually 12 months) before any equity vests
Fonzi AI educates candidates on these basics during Match Day prep. This way, you’re not seeing terms like “409A FMV” or “ISOs vs NSOs” for the first time when an offer lands in your inbox.
Types of Startup Equity Engineers See in Offers
Different equity instruments appear at different company stages, and each carries distinct legal and tax implications. Knowing what you’re receiving helps you evaluate the true value of any equity package.
Common Stock vs. Preferred Stock
Founders and employees typically receive common stock (or options that convert to common stock). Investors, venture capitalists, and angel investors receive preferred stock with special rights like liquidation preferences.
The key difference: if a startup sells for less than investors put in, preferred stockholders get paid first. Common stockholders (that’s you) might receive nothing from that exit. This is why understanding the cap table and investor preferences matters.
Stock Options: ISOs and NSOs
Most early-stage employees receive stock options. These come in two varieties:
Incentive Stock Options (ISOs): Available only to U.S. employees. ISOs offer potential favorable tax treatment; if you hold shares for at least one year after exercise and two years after grant, gains are taxed as capital gains rather than ordinary income. However, exercising ISOs can trigger alternative minimum tax obligations.
Non-Qualified Stock Options (NSOs): Broader use, including for contractors and advisors. The spread between the strike price and the fair market value at exercise is taxed as ordinary income immediately.
Restricted Stock Units (RSUs)
RSUs grant you actual shares upon vesting, no strike price to pay. The catch: RSUs trigger income tax when they vest, based on the share’s fair market value at that moment. RSUs are more common at later-stage startups (Series B/C and beyond) where valuations are higher and more stable.
How Vesting, Cliffs, and Exercise Really Work
Vesting is how you earn equity over time, not upfront. Most startups use a four-year schedule with a one-year cliff: nothing vests in year one, 25 percent vests at month 12, and the rest vests monthly until year four.
If you leave before the cliff, you get nothing. Leave after, and you keep only what’s vested. Vesting gives you the right to buy shares, but exercising means paying the strike price out of pocket, sometimes tens of thousands of dollars, with no guarantee of liquidity.
One of the most overlooked terms is the post-termination exercise window. Many plans give you just 90 days after leaving to exercise or lose your equity. More engineer-friendly startups offer one to five years, which can matter more than the headline equity number.
Comparing Startup Equity to FAANG RSUs: A Practical Table
Public-company RSUs fundamentally differ from startup equity. At Google or Meta, your RSUs vest into liquid shares you can sell on the open market at a known price. Startup stock options vest into shares you can’t sell, often for years, at a price that’s theoretical until an exit happens.
That said, startup equity can deliver dramatically higher returns. If a 2025 AI startup grows 50x by a 2030 exit, your 0.5% stake transforms from $100,000 in paper value to $5,000,000 in real money. FAANG companies' stock rarely 50x in five years.
The question isn’t which is “better”, it’s which matches your risk tolerance, financial situation, and career goals. This comparison table helps frame the tradeoffs.
Sample Comparison Table: FAANG RSUs vs Startup Equity
Factor | FAANG Offer (Senior ML Engineer) | AI Startup Offer (Series A, 2025) |
Company Stage | Public, mature | Series A, 18 months old |
Cash Base | $190,000/year | $165,000/year |
Equity Instrument | RSUs ($110,000/year) | ISOs, 0.5% ownership |
Current Equity Value | ~$110,000/year (liquid) | ~$300,000 paper (0.5% of $60M) |
Liquidity | Immediately upon vesting | Exit-dependent (5-7 years typical) |
Typical Upside (5 years) | 1.5-2x if stock grows | 10-100x if the startup exits well |
Risk Level | Low (established business) | High (90% of startups fail) |
Your Impact | One of 10,000+ engineers | One of 15 engineers, high visibility |
Best-Case Value (2030) | ~$600,000-800,000 total | $5,000,000+ if the company exits at $1B |
Note: These numbers are illustrative. Actual values vary by geography, role, and funding climate. Fonzi helps engineers gather specific data from companies to model their own scenarios.
Key takeaways from this comparison:
FAANG RSUs provide predictable, liquid value; you know what you’re getting, and you can spend it when it vests
Startup equity trades certainty for upside; the spread between best-case and worst-case outcomes is enormous
The “right” choice depends on your personal cash flow needs, risk tolerance, and belief in the specific startup
How Dilution, Valuation, and Exit Shape Your Real Ownership
Your equity stake isn’t static. Every time a startup raises a funding round, new shares enter the cap table, and your ownership percentage shrinks, even though your absolute share count stays the same.
Dilution in Practice
Say you join an AI startup at Seed stage with 0.5% equity. The company then raises Series A, B, and C rounds, each time issuing new shares to risk investors. Here’s how dilution typically plays out:
Seed (your grant): 0.50% ownership
After Series A (20% dilution): 0.40% ownership
After Series B (20% dilution): 0.32% ownership
After Series C (15% dilution): 0.28% ownership
You still own the same number of shares, but the fully diluted share count has grown. Your ownership percentage dropped by nearly half.
Why Dilution Isn’t Always Bad
Here’s the critical insight: dilution reduces your percentage, but the company’s valuation usually increases with each round. Let’s walk through a scenario for an LLM infra engineer:
Seed stage: 20,000 options, 0.25% ownership, $40M post-money valuation
Your paper value: $100,000 (0.25% × $40M)
After Series C: 0.14% ownership (diluted), but the company is now valued at $800M
Your paper value: $1,120,000 (0.14% × $800M)
Your percentage ownership shrank, but the dollar value grew 11x because the company grew 20x. This is how startup equity creates wealth, not by preserving percentages, but by growing the overall pie.
Liquidation Preferences and Exit Outcomes
When a company exits, investors with preferred stock often have liquidation preferences, typically 1x, meaning they recover their investment before common stockholders see anything.
Consider a startup that raised $50M total and sells for $60M:
Investors (preferred): Receive $50M (their 1x preference)
Common stockholders (founders, employees): Split the remaining $10M
Your 0.25% stake: Worth $25,000, not the $150,000 you expected at $60M
This is why understanding the company’s valuation and investor preferences matters. In a “soft landing” acquisition at sub-$100M, employee equity often gets squeezed by preferences.
Exit Scenarios Engineers Should Model
Not all exits are created equal. When evaluating equity offers, model these scenarios:
Exit Type | Valuation Range | Typical Employee Outcome |
$20M-$100M | Often minimal after preferences | |
Solid exit | $300M-$800M | Meaningful wealth, potentially life-changing |
Large exit | $1B+ | Significant wealth creation |
IPO | Varies | Liquidity + potential long-term growth |
Failure | $0 | Complete loss of equity value |
Fonzi encourages startups to share cap table basics and preference structures in late-stage discussions. The platform’s concierge team can coach you on which questions to ask, such as “What’s the current fully diluted share count?” and “What are investor liquidation preferences?”
How to See Multiple Equity Offers at Once
Navigating startup equity across multiple companies simultaneously is difficult when each uses different terminology, presents information differently, and operates on different timelines. Fonzi AI’s Match Day solves this problem by creating a structured, transparent process.
How Match Day Works
Match Day is a time-boxed hiring event connecting pre-vetted AI engineers with curated startups. Here’s the step-by-step process:
Application and vetting: Fonzi reviews your coding profiles, past work, GitHub contributions, research papers, and professional experience. The evaluation uses bias-audited rubrics to ensure consistent assessment.
Profile curation: Approved candidates receive enhanced profiles highlighting their technical strengths and experience level.
Company matching: Fonzi curates AI startups, typically 2025 Seed and Series A companies in LLM tooling, AI infrastructure, and applied ML, that match your background and preferences.
Compensation transparency: Companies joining Match Day must commit to salary bands and equity ranges upfront. You see cash + equity expectations before investing time in interviews.
48-hour matching window: During Match Day, matched companies review your profile and reach out. The condensed timeline creates urgency on both sides, often resulting in multiple conversations and offers within days.
How Fonzi Uses AI in Hiring
Many hiring platforms use AI as a blunt filter: keyword scans, auto-rejections, and opaque scoring that often reinforce existing bias. Fonzi AI uses AI differently, as infrastructure, not a gatekeeper.
AI is applied only where it increases signal or removes busywork:
Fraud detection to catch fake profiles and inflated credentials early
Skill signal extraction from real work like code, research, and shipped systems, not resume keywords
Structured rubrics to apply consistent evaluation standards across candidates
Scheduling automation to cut time-to-interview without cutting humans out
Evaluation systems are regularly tested for demographic skew to eliminate bias in recruitment. When candidates with equivalent skills receive different outcomes, the models are adjusted. This approach counters the bias that traditional ATS tools introduce against non-traditional backgrounds and lesser-known institutions, keeping assessments fair, consistent, and skills-based.
Preparing for High-Signal Startup Interviews (and Talking Equity Like a Pro)
Succeeding in startup interviews, especially at AI companies, requires both technical preparation and financial literacy. Here’s how to excel at both.
Technical Preparation Tips
For AI/ML and infra roles, focus your preparation on:
System design for LLM infrastructure: Be ready to discuss how you’d architect training pipelines, inference systems, and data processing at scale
Recent research implementation: Walk through papers you’ve read, and how you’ve applied them; specifics matter more than breadth
AI engineer portfolio presentation: Prepare GitHub repos, Colab notebooks, or demos that showcase your strongest work
Quantified impact: Frame past contributions with metrics (“Reduced training time by 40%,” “Improved model accuracy from 82% to 91%”)
Modeling Your Outcomes
For each offer, create a simple model with 2-3 scenarios:
Scenario | Company Valuation | Your % | Your Value (pre-tax) |
Conservative (1x) | $60M (no growth) | 0.5% | $300,000 |
Moderate (5x) | $300M | 0.35% (diluted) | $1,050,000 |
Aggressive (20x) | $1.2B | 0.25% (diluted) | $3,000,000 |
Then compare not just total dollars, but also:
Risk (company stage, market, team quality)
Impact (your role in the company’s growth)
Learning (technical challenges, career compounding)
Timeline (when can you realistically expect liquidity?)
Summary
Startup equity can be life-changing or meaningless, depending on whether you understand its mechanics. Vesting, dilution, liquidation preferences, and taxes aren’t fine print; they determine the real value of your offer. Engineers who learn to evaluate equity clearly avoid costly mistakes and capture upside when it actually exists.
For AI and ML engineers in 2025, the timing matters. Infrastructure, LLM tooling, and applied AI startups are well funded, and competition for senior talent is pushing equity packages higher. Those who can compare cash and ownership rationally are best positioned to benefit over the next 3–5 years.
Fonzi AI is built to make this process transparent and fair. Startups commit to salary and equity ranges upfront, evaluations are bias-audited to eliminate bias in recruitment, and Match Day provides concierge support so candidates can compare multiple real offers side by side.
FAQ
How does equity work in a startup compared to FAANG RSUs?
What’s the difference between stock options and RSUs in a startup offer?
How does vesting work for startup equity, and what is a cliff?
What percentage of equity should I expect as a senior engineer at a startup?
Do I have to pay for my startup equity, and how much will it cost?
