Most AI and tech roles ask for five or more years of experience, and that share is growing. In tech job postings that state a requirement, the share wanting at least five years rose from 37% to 42% between 2022 and 2025, according to Indeed’s Hiring Lab. Entry-level postings barely moved, holding near 18%.
The headline number hides the useful part. Experience requirements are not fixed, they are drifting upward, and the roles disappearing are the mid-career ones that used to be the natural second job.
How many years of experience do AI jobs ask for?
The years of experience AI and tech jobs ask for split three ways across US postings that name a figure.
Two in five postings want five years or more. Another two in five sit in the two-to-four-year band. Fewer than one in five are open to someone with a year or less. For AI roles specifically the centre of gravity is, if anything, higher, because machine learning and applied research reward the kind of judgement that only comes from having shipped a few systems.
Our own reading of the market matches the external picture. In an analysis of 678 live AI roles from 188 companies hiring across Europe, five years was the single most common figure named, and 55% of the roles that state a figure asked for five or more. That lines up with Indeed’s finding that the market has settled above the mid-point.
Are experience requirements getting stricter?
Experience requirements are getting stricter, and the shift is recent and measurable.
Indeed’s Hiring Lab compared the second quarters of 2022 and 2025 and found the market pulling toward seniority at the expense of the middle.
| Experience asked for | Share of postings, 2022 | Share of postings, 2025 | Change |
|---|---|---|---|
| 0 to 1 years | 17% | 18% | +1 |
| 2 to 4 years | 46% | 40% | -6 |
| 5 or more years | 37% | 42% | +5 |
Source: Indeed Hiring Lab, US tech occupations.
The two-to-four-year band lost six points in three years. That is the squeeze people feel when a role they could have landed in 2022 now lists a number one notch higher. Hiring freezes concentrate what openings remain on people who can deliver without ramp-up, and the first thing an employer trims under pressure is the budget for someone who needs teaching.
Do you really need five years for an AI job?
Five years is not always a real requirement for an AI job, because the stated number and the real bar are different things.
A listing that asks for five years is usually describing a level of independence, not counting the calendar. Employers reach for a round number because it is easy to write, not because they measured it. Evidence that you can work without supervision travels better than the years themselves. Someone who has shipped a retrieval system into production and can explain what broke will clear a five-year bar that a candidate with six uneventful years cannot.
The supply side helps explain why the bar keeps rising. Stack Overflow’s 2025 developer survey found that 24.8% of its 42,763 respondents have between one and five years of professional experience. Roughly a quarter of the profession sits at or below the most common requirement, so every employer holding firm at five years is competing for the same limited pool.
Why is it so hard to get an entry-level AI job?
Entry-level AI jobs are hard to get because the growth in hiring is concentrated at the top, not the bottom.
Indeed’s Hiring Lab found that 71% of the increase in software development postings between May 2025 and May 2026 came from senior roles, with 37% from jobs that name AI in the title. Demand is not just growing, it is growing at the senior end, while the junior rungs that once let people climb in are thinner than they were.
This is the entry-level paradox in one line. The roles that ask for no experience are the ones companies are least willing to add during a freeze, because a junior hire is an investment that pays back later, and later is exactly what a cautious budget will not fund.
How do you get an AI job without the years?
Getting an AI job without the years comes down to targeting the openings that cannot screen you out on a number, and bringing evidence instead of tenure.
Three moves do most of the work.
- Apply to the roles that state no figure. A large share of postings never name a number, and none of them can reject you on one. These are the openings where a strong portfolio does the talking.
- Treat a gap of a year or two as noise. The distribution shows employers picking round numbers, not measuring. A one-year gap against a five-year ask is worth an application. A five-year gap against a ten-year ask is not.
- Lead with shipped work. The field rewards demonstrated range over time served more than most. A public project that solves a real problem answers the experience question before it is asked.
Read the tools a listing names rather than the years at the top of it, then answer the tools. A role naming retrieval, evaluation, and production LLM work wants someone who has done those things, and can be convinced by proof regardless of the number beside “experience”.
Start with remote AI engineer jobs and remote machine learning jobs, which carry the most openings. Data science and AI research sits alongside them, and every AI role open across Europe is the widest view of what is live.