We Analyzed 35,000 AI Startup Job Listings. Only 1.3% Are Entry-Level.
By Fast AI Startup Jobs
The AI job market is booming. Funding keeps pouring in, and every week another startup announces a new model, a new product, or a new round. If you're early in your career, you're probably wondering where you fit in.
We ran the numbers, and the answer is uncomfortable.
What the numbers say up front
Only 458 out of 35,450 AI startup jobs, about 1.3%, are explicitly tagged as intern or new-grad level. The rest are listed as experienced. That label is crude, though, and many of those roles may only ask for 1 to 2 years of relevant work, so the genuinely accessible number is probably higher. The data just can't tell us by how much.
Within that 458, software engineering is by far the largest entry-level category, with 197 roles, or 43% of all entry-level openings. Early-stage companies are your best shot: Series A and Seed firms hire entry-level candidates at roughly 3 to 5 times the rate of late-stage or growth-equity-backed companies. And 76.9% of these "entry-level" roles are full-time positions rather than internships, which means companies do want permanent junior staff, just very selectively.
What 35,000 AI startup jobs actually look like
Our dataset covers 35,450 job listings across 1,542 AI startups, gathered from the Fast AI Startup Jobs dataset of active AI companies. These aren't legacy enterprise jobs or Big Tech roles. They're the startups building at the frontier, the companies job seekers most want to break into.
The headline finding:
| Category | Count | Share |
|---|---|---|
| Experienced (any level) | 34,244 | 96.6% |
| Entry-level / Intern / New-Grad | 458 | 1.3% |
| Unlabeled / Unknown | 748 | 2.1% |
One point three percent deserves scrutiny and context.
It does not mean 98.7% of AI startup jobs are inaccessible to early-career candidates. "Experienced" is a crude default label in most ATS systems, applied to everything that isn't explicitly flagged as an internship or new-grad program. Many roles tagged "experienced" may only require 1 to 2 years of relevant work. The real number of accessible jobs is likely higher, but the data can't tell us by how much.
What the 1.3% does reveal is that AI startups are not building structured junior hiring pipelines. The dominant model is hire seniors, move fast, ship fast. Formal entry-level programs, the kind with cohort onboarding, mentorship tracks, and rotational assignments, are rare. When juniors get hired, it tends to happen opportunistically rather than systematically.
That doesn't make it impossible. It means you need to be strategic in a way that most job seekers aren't.
Where the 458 entry-level jobs actually are
Not all entry-level opportunities are created equal. The breakdown by department shows where companies are actually willing to take a chance on junior talent.
| Department | Entry-Level Jobs | Share of 458 |
|---|---|---|
| Software Engineering | 197 | 43.0% |
| General / Unclassified | 91 | 19.9% |
| Research | 31 | 6.8% |
| Marketing & Growth | 22 | 4.8% |
| Analytics | 20 | 4.4% |
| Sales & Partnerships | 15 | 3.3% |
| Infrastructure & Platform | 10 | 2.2% |
| Recruiting & Talent | 10 | 2.2% |
| ML / AI Engineering | 9 | 2.0% |
| Design | 8 | 1.7% |
| Data Science | 8 | 1.7% |
Nearly half of all entry-level openings are in software engineering, which makes sense. SWE roles have the most mature hiring pipelines for junior candidates, with structured onboarding playbooks borrowed from FAANG and well-defined career ladders. But note what's nearly absent from the list. ML/AI Engineering has only 9 entry-level slots across 35,000 jobs, and Data Science has 8. If your plan was to land a junior ML engineer role at an AI startup straight out of school, the data says that path is extremely narrow. Not impossible, but narrow.
The "General / Unclassified" bucket is worth a closer look. Those 91 roles (19.9%) are jobs where the company didn't bother with department tags, often early-stage startups posting their first few hires. That's a signal worth tracking. Unstructured listings at young companies frequently represent the most flexible roles, where responsibilities are still being defined and a strong candidate can shape the position.
Non-technical roles tend to get overlooked, too. Marketing & Growth (22), Analytics (20), Sales & Partnerships (15), and Recruiting (10) together account for about 15% of all entry-level openings, and candidates fixated on engineering or research often skip right past them. For context, our analysis of the full dataset shows that 46.6% of all AI startup jobs, not just entry-level, sit outside engineering departments. The non-technical side of AI is far larger than most people assume, and entry-level openings exist there too.
Funding stage is the most actionable signal
This is the finding you can act on most directly.
We looked at the percentage of each company's job listings tagged entry-level, broken out by funding stage:
| Funding Stage | Entry-Level % of Total Jobs |
|---|---|
| Series A | 2.1% |
| Seed | 2.0% |
| Series B | 1.8% |
| Series F | 1.5% |
| Series E | 1.2% |
| Series G+ | 0.7% |
| Series C | 0.6% |
| Series D | 0.5% |
| Growth (PE / Late) | 0.4% |
| Pre-Seed | 0.0% |
| Bootstrapped | 0.0% |
The pattern is clear. Series A and Seed companies hire juniors at roughly 3 to 5 times the rate of late-stage companies.
The logic isn't hard to follow. A Series A company with 15 to 40 employees has figured out its core product direction but still needs to scale its team fast. Juniors are cheaper, easier to mold to the company's culture, and a long-term talent investment. A Series D company with 300+ employees is optimizing for execution speed instead. It wants people who can contribute from week one, no runway required.
The two extremes are worth noting. Pre-seed and bootstrapped companies show 0.0% entry-level hiring. Pre-seed teams are typically 2 to 5 founders who need every hire to carry an unrealistic amount of weight, and bootstrapped companies have limited capital and can't absorb the cost of junior onboarding. Don't target either for your first AI job.
In practice, this means filtering your search toward Series A and Seed companies. They're harder to find than the Anthropics and OpenAIs of the world, but the math is firmly in your favor by comparison.
How strict is the "experienced" label really?
Here's a caveat the raw data can't fully answer: what does "experienced" actually mean in a job posting?
In many cases it's applied to any non-intern role as a default, including positions that only ask for 1 to 2 years of relevant work in the requirements. Companies frequently use "experienced" without meaning "we want 5 years." Some jobs tagged "entry-level," meanwhile, come with skills requirements that would challenge a mid-career professional.
The label is a filter, not a law. Job descriptions that list 2 or fewer years of experience, or use language like "strong fundamentals," "passion for X," or "ability to learn fast," are often genuinely accessible to early-career candidates even when they're filed under "experienced" in a database.
So the real number of AI startup jobs open to early-career candidates is likely higher than 1.3%. By how much is impossible to say without reading every job description. What the data does confirm is that startups are not building junior pipelines at scale. When entry is possible, it tends to come through direct outreach, referrals, or unconventional positioning rather than the standard application queue.
What this means for your search
If you take anything practical from these numbers, start with funding stage. Your base rate of finding an entry-level opening is roughly 2% at Seed and Series A versus 0.4% to 0.7% at late-stage companies, a 3 to 5x difference, so weight your search toward companies funded within the last 6 to 18 months. They're in hiring mode, and their entry-level odds are the highest in the dataset.
Software engineering is your widest funnel. With 197 of the 458 entry-level openings (43%), any engineering background, even a bootcamp or a strong portfolio, gives you the most surface area to apply against. Once you're inside, pivoting toward ML, data science, or product is far easier than breaking in cold.
Don't skip the 91 unclassified listings either. They cluster at earlier-stage companies posting their first hires in a given function, which makes them less competitive, more flexible on requirements, and often closer to the founding team. They're the hidden inventory of the entry-level AI job market.
For non-technical entry points like marketing, analytics, sales, and recruiting, our companion analysis goes deeper: 47% of AI Startup Jobs Aren't Engineering Roles.
Methodology
The data in this article comes from the Fast AI Startup Jobs dataset of 35,450 AI startup job listings across 1,542 active companies. Jobs were classified as "entry-level" or "intern/new-grad" based on job title tags, seniority labels, and listing metadata. "Experienced" was assigned to all other roles. Funding stage data was sourced from company profiles in the same dataset. The snapshot reflects job postings collected as of early 2026.
The department breakdown covers all 458 entry-level jobs; percentages are rounded to one decimal place and may not sum to exactly 100%.
A few limitations to keep in mind. The dataset is sourced from major ATS platforms (Ashby, Greenhouse, Lever, and others). Companies that post jobs only on their own websites, or use ATS platforms we don't track, are not represented. That skews the data toward companies that have adopted structured hiring tools, generally companies with 10+ employees. Very early-stage teams hiring via Twitter DMs or personal networks are invisible here.
Job-level tagging is also inconsistent across companies, and the "experienced" label is applied inconsistently by employers. The 1.3% figure represents explicitly tagged entry-level openings, so the true accessible population may be somewhat higher. Analysis of job description text requirements was outside the scope of this dataset query.
Data sourced from the Fast AI Startup Jobs dataset. Analysis reflects 35,450 listings across 1,542 companies.
Related: 47% of AI Startup Jobs Aren't Engineering Roles. Here's the Data.. Our analysis of the same dataset shows that nearly half of all AI startup roles sit outside engineering departments.