Will Sanders
In 2026, the median base salary for a Machine Learning Engineer at an AI startup is $200,000. Our data from 122 Machine Learning Engineer roles tracked over the last 30 days shows the 25th percentile at $184,000 and the 75th percentile at $249,000 for base compensation. This reflects strong demand across companies like Affirm, X9, and Plaud, indicating robust compensation for skilled ML talent.
The typical ml engineer salary at an AI startup in 2026, when considering base compensation alone, ranges from $184,000 to $249,000, with a solid median of $200,000. This is based on recent roles we've tracked. Total compensation, including equity and bonuses, often pushes these figures significantly higher, especially at later-stage startups or those with strong funding and clear paths to liquidity.
From my 12 years in technical recruiting, I've seen countless compensation packages. For Machine Learning Engineers, the market for top talent remains fiercely competitive. AI startups, especially those building foundational models or applying ML to complex, high-value problems, are willing to pay for expertise. They have to. The alternative is falling behind.
Our data from 122 machine learning engineer roles over the last 30 days confirms this. We saw a median base salary of $200,000. The 25th percentile was $184,000, and the 75th percentile reached $249,000. These figures represent base salary and don't yet factor in equity, which can be the most significant component of total compensation at a startup. Companies like Affirm, X9, Striveworks, Plaud, Xometry, and Craft Ventures were among those actively hiring for these roles. These aren't just seed-stage companies; they span various growth stages, from well-funded Series A/B to more established public entities that still operate with a startup mindset in their AI divisions.
It’s important to understand that "AI startup" is a broad term. A 10-person seed-stage company with $2M in funding will compensate differently than a Series C company with 300 employees and $100M raised. Both are technically "AI startups," but their risk profiles and ability to pay differ. The data we're seeing reflects a mix of these stages, skewed towards companies with proven funding and traction.
When we talk about salary, it's not just the number. It's the entire package. Base salary is the stable component. Equity, however, is where the significant upside, and downside, lies. For an ML Engineer evaluating an offer, understanding the company's valuation, funding runway, and the equity's vesting schedule is as critical as the base.
Experience level significantly dictates an ML Engineer's salary, with compensation increasing substantially from junior to staff or principal roles. A junior ML engineer might start near the lower end of the $184,000 to $249,000 range for base salary, while a staff-level engineer can easily exceed the $249,000 mark.
Here's how I typically see it break down across different levels, based on the roles we place at Recruiting from Scratch:
Junior ML Engineer (0-2 years experience): These are often recent graduates or those transitioning from other engineering disciplines. They have strong theoretical knowledge but limited practical deployment experience.It's critical for engineers to understand that these figures are broad strokes. A Staff ML Engineer at a seed-stage AI startup might have a lower base but significantly higher equity than a Staff ML Engineer at a Series C company with hundreds of employees. The equity at a seed stage is riskier but offers more potential upside if the company scales.
When we're proactively sourcing candidates, we always discuss their career goals and risk appetite. Some engineers prioritize a higher, more stable base salary, while others are chasing the exponential growth potential of early-stage equity. There's no single right answer. It depends on where you are in your career and what you value.
Geographic location and the rise of remote work significantly influence ML Engineer compensation, with Silicon Valley and New York City commanding the highest salaries, while remote roles typically offer slightly less but with a broader talent pool. A San Francisco-based ML Engineer might see a 10-20% higher base salary compared to a similar role hired remotely or in a lower cost-of-living area.
In my experience, the Bay Area and New York City remain the top-paying markets for technical talent, including ML Engineers. This isn't just about cost of living; it's about the concentration of capital, innovation, and other top-tier talent. Companies in these hubs compete fiercely for engineers.
Here's a general breakdown I've observed:
| Location | Base Salary Index (SF Bay Area = 100%) | Typical Base Range (Senior ML Eng) |
| :------------------- | :------------------------------------- | :--------------------------------- |
| SF Bay Area / NYC | 100% | $220,000 - $260,000+ |
| Seattle / Boston | 90-95% | $200,000 - $240,000 |
| Austin / LA | 85-90% | $190,000 - $230,000 |
| Remote (US) | 80-90% | $180,000 - $220,000 |
| Other US Cities | 75-85% | $170,000 - $210,000 |
It's important to clarify "Remote (US)." Many companies that hire remotely have location-based pay bands. This means if you're working remotely from, say, Omaha, Nebraska, your salary might be indexed to a national average or a specific regional tier, rather than the San Francisco rate. Some companies, especially those that are "remote-first," have a single pay band for the entire US, which often falls somewhere in the 85-90% range of an SF salary.
The pandemic accelerated the acceptance of remote work, and many AI startups now embrace it. This has opened up talent pools across the country, but it also means engineers from high cost-of-living areas might need to adjust their expectations if they move to a lower cost area and keep their "Bay Area salary" expectations. Companies, particularly startups, are cost-conscious. If they can get comparable talent at a 15% discount by hiring remotely outside of a tech hub, they often will.
However, for some highly specialized ML roles, particularly those requiring rare expertise in niche areas of AI, location might become almost irrelevant. If you're one of a handful of people globally who can solve a specific, hard problem, a company will pay top dollar regardless of where you live, within reason. These are the exceptions, not the rule.
When we work with a client, we help them define their compensation philosophy for remote versus in-office roles. For engineers, my advice is to understand the company's specific policy. Don't assume a "remote" role means a "Bay Area equivalent" salary unless explicitly stated. Ask the recruiter directly: "Is compensation location-based or does this role have a single national pay band?"
Beyond base salary, an ML Engineer's total compensation package at an AI startup includes equity, performance bonuses, and a range of benefits. Equity, typically in the form of stock options or RSUs, often represents the largest potential value in a startup package.
This is the big one for startups.
The potential value of equity can be enormous if the startup succeeds, but it's also illiquid until an IPO or acquisition. Engineers need to understand the company's current valuation, projected growth, and the dilution risk from future funding rounds. We always encourage candidates to ask about the total number of shares outstanding and what percentage their grant represents. A smaller percentage of a high-value company can be worth more than a larger percentage of a low-value company.
Many startups, even early-stage ones, offer a discretionary or performance-based bonus. This is typically a percentage of your base salary, often 10-20%, paid annually based on individual and company performance. At AI startups, these bonuses are often tied to hitting specific product milestones, model performance metrics, or overall company growth targets. They are less common at seed stage but become more standard as companies reach Series B and beyond.
Standard benefits include:
When evaluating an offer, look at the total package. A higher base salary might seem appealing, but if the equity is negligible or the benefits are poor, the overall value might be less than an offer with a slightly lower base but substantial equity and strong benefits.
An AI startup's funding stage significantly affects ML Engineer salaries, primarily through the risk-reward profile of the equity component. Seed-stage companies offer lower base salaries but potentially higher equity percentages, while later-stage companies (Series B, C, or public) provide higher, more stable base salaries and potentially more valuable, but smaller, equity grants.
I've helped place ML Engineers at everything from 10-person seed startups to large public companies like Palantir. The compensation structure shifts dramatically across these stages.
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