AI Startup Total Compensation Benchmarks by Funding Stage

Total compensation at AI startups is not one market with one set of rules. The ratio of cash to equity, the absolute dollar amounts on offer, and even what counts as "competitive" shift dramatically depending on whether a company is pre-seed or Series C+. Engineers who judge every startup offer by the same yardstick will misjudge most of them.

The cost of that mistake runs in both directions. If you judge a pre-seed offer by a Series B comp table, you'll walk away from a strong early-stage package just because it looks thin in cash. Flip it around: an engineer who benchmarks a Series B offer like it's still pre-seed will accept a late-stage package that quietly undervalues their market rate in cash. The AI hiring market makes this sharper, not softer: benchmarks from H1 2025 show AI and ML roles commanding a real premium over equivalent non-AI roles. The AI/ML premium appears unevenly across stages depending on where a company sits in its funding life, and treating it as a flat bonus across every offer will lead an engineer to the wrong conclusion about what's actually on the table.

Funding stage determines the cash-vs-equity ratio

The split between cash and equity at any startup is largely fixed by how much capital the company has raised and what it costs to win talent at that point in its life. A company with little cash on hand compensates with a bigger slice of ownership. A company that has raised more, and carries a higher valuation, can pay closer to market in cash, and its equity grants shrink in percentage even as the paper value behind them grows.

Stage-level salary data backs this up, too, but on the cash side. The gap closes on the equity side and widens on the cash side as a company matures.

The AI labor market adds another layer to this. Those startups can't pay what a lab pays at that ceiling, so they compete on equity percentage instead, and that has pushed equity grants up for AI-native technical roles specifically. Consider a founding AI engineer joining at pre-seed versus a staff engineer joining at Series B: the first is largely betting on equity because the cash isn't there to compete with a lab offer, while the second is being paid close to market in cash precisely because the company can now afford it. An engineer evaluating a pre-seed offer needs to be solving for equity quality first. An engineer evaluating a Series B offer should be solving for base salary first, because the equity percentage at that stage will be small no matter how well they negotiate.

Pre-seed and seed equity benchmarks for AI engineers

At pre-seed and seed, equity percentage is the main lever a company has to compete for talent, and the range for AI-native technical roles is wide enough that the gap between a strong offer and a weak one runs several percentage points, not fractions of a point.

A 0.25% grant for a senior IC at pre-seed is on the low end of what's reasonable. A 1.5% grant for a founding AI engineer at that same stage is genuinely strong, so not every pre-seed offer looks the same.

Base salary at this stage is a below-market tradeoff by design. Founding engineers at pre-seed companies take less cash so they can get a larger equity grant, and that tradeoff directly reduces their near-term income. It should be priced directly into how the equity is evaluated rather than treated as a separate, unrelated fact. What makes a grant competitive at this stage isn't the percentage alone. A founding engineer should also know, at minimum, an approximate fully diluted share count, because a percentage with no denominator attached tells almost nothing about what the grant is actually worth.

Series A benchmarks: where cash and equity are both still meaningful, and the founding-engineer distinction matters most

Series A is where total compensation turns into a genuine two-variable problem. Base salary has risen enough that it matters on its own merits, and equity percentages are still large enough that title and role distinctions, especially founding engineer versus standard senior hire, produce meaningfully different packages.

The founding-engineer designation still carries real weight here. A genuine first or second engineering hire joining at Series A can reasonably command equity at the higher end of the founding-engineer band, 0.2% to 0.75%, rather than settling for the standard senior IC band of 0.05% to 0.35%. You can negotiate that gap explicitly instead of accepting whatever title the offer letter defaults to, because it's large enough to matter. The same logic applies to a VP Engineering or Head of AI joining at Series A to build a team from scratch: a genuine team-building mandate justifies treatment closer to the Head of AI band than the standard VP band. Series A is also where vesting terms start to separate strong offers from mediocre ones, and you can realistically ask for double-trigger acceleration and extended exercise windows. You'll get the full mechanics of negotiating those terms later, but it's flagged here first.

Series B and Series C+ benchmarks: when cash becomes the primary signal and equity percentage compresses sharply

At Series B and beyond, the structural logic of the offer flips. Base salary converges toward market rate, equity percentages compress into a fraction of what they were at seed, and the right question changes from "is my equity percentage strong?" to "is my cash package competitive, and does the equity's paper value actually reflect the company's real valuation?"

Base salary benchmarks for both stages carry the same AI/ML premium seen earlier: you can add 15% to 25% on top of the baseline figures. At this stage, annual equity refreshes are a standard part of the package rather than an exception, with refresh grants commonly representing a substantial share of the size of the initial grant as a retention mechanism. If you're evaluating a Series B or later offer, ask about refresh cadence with the same seriousness you apply to the initial grant.

The right way to read equity at this stage isn't about the percentage in isolation. At a company with significant revenue and a credible path to IPO or acquisition, a small percentage can carry substantial paper value, so the company's valuation and trajectory determine what the equity is actually worth, not the headline number. Late-stage startups also pay senior engineering talent meaningfully more in base salary than their early-stage counterparts, and that cash premium is the main reason engineers with strong market value often choose later-stage opportunities even when the equity upside is smaller.

How AI-native roles are benchmarked versus generalist software engineers

Working as an ML engineer or research scientist at an AI startup puts an engineer in a different market than working as a general software engineer at the same company. The title and the underlying skill set carry a premium that holds regardless of funding stage, and that premium compounds on top of every benchmark already laid out above.

Role distinctions inside AI matter on the equity side as well as the cash side, not just at the level of base salary. The "AI engineer" title in particular is now the fastest-growing job title in the US by posting volume, and that kind of growth signals a supply and demand picture that's actively shifting. Frontier lab compensation sets a useful reference ceiling, but don't expect a startup to match that number. Senior, L5-equivalent engineers at OpenAI, Anthropic, and Google DeepMind are paid at a level no pre-seed or seed startup can match in cash. That gap is what pushes equity percentages upward for AI-native roles at the earliest stages. It's useful context for understanding why the benchmarks above look the way they do, not a target to measure a startup offer against directly.

Geographic adjustments that change what any benchmark number means

Every benchmark in this piece is set at a Tier 1 tech-hub baseline, so it means major coastal tech hubs. If you're based elsewhere, or evaluating a remote offer, you need to apply a geographic multiplier before you treat any of these figures as a personal reference point.

That adjustment is shrinking in significance, though not disappearing. Remote roles at well-funded AI startups increasingly price at national band rates rather than adjusting down for location, and the gap between SF-based pay and remote pay has been closing. Still, companies vary in how they handle this, so you should confirm directly whether a given company uses geographic tiers or a flat national band before you try to benchmark an offer against the figures above. If you apply a Tier 1 number to a remote offer priced on a lower geographic tier, or the other way around, the comparison means nothing.

Why equity is worth less than it looks

A competitive equity grant and a valuable equity grant are two different things. Dilution, the probability the company ever reaches an exit event, and the cash salary given up to earn the grant in the first place together set whether the equity math favors the engineer making the bet.

Dilution is a structural feature of raising multiple financing rounds. A typical startup raises several more rounds of financing after you join, and each round dilutes existing shareholders. A founding engineer's seed-stage grant can end up a meaningfully smaller fully diluted percentage by the time any exit happens, even if the company does everything right along the way. 2026 startup salary benchmarks are blunt about the odds here too: early-stage equity has a very high chance of being worth nothing in the end. That risk needs to be factored in before combining equity face value with salary into a single total comp number.

Before you accept any equity offer, find out the total number of fully diluted shares, because a percentage without that denominator tells you almost nothing. The current 409A valuation matters too, because it sets the exercise price for options. The exercise window after departure deserves particular attention: legacy 90-day windows function as a hidden cost that can force a former employee to either exercise immediately or lose the options entirely, so negotiating for a 7 to 10 year window where possible is worth real effort. A well-understood equity package, even a modest one, is worth more to an engineer than a headline percentage nobody has pressure-tested.

Using these benchmarks when evaluating or negotiating an offer

The benchmarks only pay off if you apply them in the right order. Engineers who try to negotiate cash and equity at the same time, without first establishing the right baseline for the company's stage, routinely leave value on the table or anchor themselves to the wrong number.

Start by identifying the funding stage and pulling the benchmark table that actually applies to it, since a seed offer and a Series B offer aren't comparable on the same terms. From there, apply the AI/ML role premium on top of the base engineering benchmark if the role is explicitly AI-native. Next, adjust for geography, converting the SF or NYC benchmark figures to the actual location in question, or to whatever pay band policy the company uses. Then separate the two halves of the offer entirely: assess cash competitiveness on its own first, and only then evaluate the equity on its own merits, using the fully diluted share count and current valuation as inputs. Risk-adjust the equity by assigning it a realistic probability of a meaningful outcome, and model that return against whatever cash is being given up below market rate, if any. Finally, negotiate the terms that companies tend to give more easily before trying to renegotiate the grant size itself: exercise window, acceleration terms, and vesting schedule all move more easily than the headline percentage.

If you're evaluating a founding-engineer offer, push on the equity percentage first, but only once the founding-level designation is actually established. Be explicit with the company about that designation, because the gap between standard senior IC equity and founding-engineer equity at pre-seed or seed is big enough to fight for. At Series B and beyond, the calculation flips: the cash negotiation carries more weight than the equity negotiation, so the focus belongs on base salary and refresh cadence rather than the initial grant percentage, which will stay small no matter how skilled the negotiation is.

Speed matters on top of all of this. An engineer who understands exactly where an offer sits against these stage-specific benchmarks can move through that process with real confidence, saving the time spent comparing numbers that were never built to be compared.