How to Get an AI Internship in College: A Step-by-Step Plan

To get an AI internship in college, pick one lane (a big lab or tech company, an AI startup, or a campus research group), build one or two public projects that match it, apply early in the recruiting cycle, and email the people who run the work directly. Research groups and startups are often the most reachable first steps.

Definition: An AI internship is a short, usually paid stint (often 10 to 12 weeks in summer, sometimes part-time during the semester) where a student works on machine learning, data, evaluation or AI product work inside a company, lab or university research group.

The good news: per Handshake's Class of 2026 workforce outlook, the share of internship descriptions on its platform that mention generative AI rose more than fourfold since 2023.

The harder news: more mentions don't mean more seats for beginners. The 2026 Stanford AI Index reports that employment for software developers aged 22 to 25 has fallen nearly 20 percent from 2024. And Gergely Orosz's Pragmatic Engineer newsletter, drawing on Live Data Technologies data in June 2026, found intern intake at tech employers still falling even as overall engineering hiring recovered. Entry-level AI work is real, but the screen is tighter, and the students who get through usually bring evidence, not just interest.

How to get an AI internship: the plan on one screen

Choose a lane. Lab, startup or research group. Each wants something different.
Pick a role. ML engineering, data and evaluation, research assistance, or a non-coding AI role.
Build proof. One or two projects you can show in two minutes, with a link.
Map the calendar. Big companies recruit early; startups and research groups hire closer to the start date.
Apply and reach out. Apply, then send a short note to the person who owns the work.
Convert. Treat the internship as a ten-week interview for a return offer.

If you change one thing, change step 3. A working demo says what a thin resume can't.

Where to find AI internships

AI internships are spread across three very different kinds of employer. Searching only one of them is a common reason students conclude there's "nothing out there."

Big AI labs and tech companies

The most visible programs, with the biggest applicant pools. Read eligibility lines closely: Google Research describes its research internships as aimed at PhD students, while its Student Researcher program is open to bachelor's, master's and PhD students. Many undergraduates reach AI teams at large companies through a software or data internship rather than an "AI intern" title. Our guide to the best AI job boards shows how to search Greenhouse, Ashby and Lever pages directly.

AI startups

Startups post fewer internships, later, and often without a clear label. A seed-stage company with no posted role may still take a strong student for the summer. The AI track of the 1752vc careers board is one place to start: filter the level to Internship and the time posted to Past 7 days; each listing shows the employer's own posting date, so stale roles are easy to skip. Y Combinator also runs an internships page for roles at its funded startups, which brings students together as a cohort with talks from founders.

University research groups

Often overlooked, and for many first-timers the most realistic. Faculty labs need help cleaning data, running experiments and building evaluation sets, and they tend to recruit through department lists or a direct email. The National Science Foundation's Research Experiences for Undergraduates (REU) program also funds summer research sites across the US. Per NSF, students apply directly to each site, participants receive stipends and in many cases help with housing, meals and travel, and most summer sites set deadlines between January and March. NSF lists eligibility as US citizens, nationals and permanent residents, so international students will want to look at campus programs instead.

What AI labs look for vs what AI startups look for

Same field, different screens. Here's how we read the gap.

What they check	Big labs and tech companies	AI startups	Campus research groups
First filter	School, coursework, structured interview performance	A shipped project or demo	Grades in key courses, a short email showing real interest
Technical bar	Coding interviews, ML fundamentals	Can you build and debug something useful fast	Can you learn their stack and run experiments carefully
What impresses	Research or competition results, strong fundamentals	Range: data, product, users, speed	Reliability and curiosity over months
Timing	Early and fixed	Rolling, close to start date	Rolling, often per semester

That's our generalization, not survey data; plenty of employers break the pattern.

For labs: depth matters. Math, statistics and solid engineering habits get you through the loop; a paper or research role lifts you out of the pile. If research is the long-term goal, our guide on how to become an AI researcher covers the PhD question in detail.

For startups: range matters. A founder at a 12-person company wants an intern who can wire a model into a product, notice what's broken and fix it without a ticket. A deployed app with 30 real users usually beats a polished notebook.

The research assistant route on campus

For first and second years, this is often the highest-odds move.

Find the right labs. Read your department's faculty pages and look for applied groups (health, climate, education, language) where a careful undergraduate can contribute quickly. Many schools run formal undergraduate research programs, some paid and some for credit, so check your research office before cold emailing.

Read, then write. Skim one recent paper from the group and email with one specific question. In large labs, a PhD student or postdoc often manages undergraduates day to day and can be a better first contact than the professor.

An illustrative email you can adapt (keep it under 150 words):

Subject: Undergraduate interested in [lab topic] research

Hi [Name], I'm a [year] studying [major] at [school]. I read your paper on [topic] and was interested in [one specific point]. I wondered how [one honest question].

I've taken [relevant courses] and built [one project with a link], where I [one concrete result].

Would you have room for an undergraduate this [semester or summer], paid, for credit or as a starting volunteer project? I can commit [hours] a week. Happy to start with any small task, such as cleaning a dataset or reproducing a baseline.

Thanks, [Name] [link]

Ask early whether it's paid, credit or volunteer. Either way, research gives you something few classmates have at internship time: a faculty reference who has watched you work.

Best AI internships for college students: types worth chasing

"Best" depends on what you want next. Our picks, by type:

Research or ML engineering intern at a lab or large tech company. The strongest resume signal and the hardest to land.
Applied AI engineering intern at an AI startup. Arguably the fastest learning per week; you'll likely ship things customers use.
Data and evaluation intern. Building test sets and evaluation harnesses. Less glamorous, and it teaches the question many teams struggle with: is the model actually any good?
REU or campus research assistant. Mentored, often paid or credit-bearing, and a strong path to graduate school.
Non-technical AI internships. Product, operations, solutions, sales and marketing roles at AI companies, a fit for business and humanities majors who can show hands-on AI fluency.

If you're not sure which role family fits you, our list of entry-level AI jobs describes what each one does day to day.

When to apply: an AI internship timeline by year

First year: core math and programming courses, one project, and emails to two or three research groups.
Second year: REU sites (most summer deadlines fall between January and March, per NSF), startup internships and any big-company programs for underclassmen.
Third year: the main recruiting year at large companies, often opening in the fall for the following summer. Keep startups and research groups on the list.
Final year: return offers and new-grad roles. Our guide to getting a tech internship covers general recruiting timelines in more depth.

Why it matters: per NACE's 2026 Internship and Co-op Survey, the average conversion rate of interns to full-time hires reached 63.1 percent for 2024 to 2025 interns, the highest in five years. With the New York Fed putting recent-graduate unemployment at about 5.6 percent in the second quarter of 2026, a return offer is worth a lot.

On pay, NACE's 2026 compensation guide puts the average bachelor's-level intern wage at $23.35 an hour across fields; technical roles at large companies often pay more. Unpaid internships at for-profit companies have to pass the Labor Department's primary beneficiary test, so we'd be wary of a startup that wants an unpaid AI intern doing real work.

"You need a PhD to get an AI internship"

Partly true. Many formal research internships at large labs are aimed at PhD students, and if you want to invent new model architectures, a doctorate is still the most common road.

But.

Most AI work isn't frontier research. It's building products on models, wiring up data and running evaluations, and those jobs hire undergraduates. Even on the research side, entry points exist: Google's Student Researcher program is open to bachelor's students, and Anthropic's Fellows Program, a four-month funded research fellowship rather than an internship, states that applicants don't need a PhD, prior ML experience or published papers. Anthropic also reports that over 35 percent of fellows from past cohorts later joined the company.

Our read: a PhD opens certain doors. It isn't the key to most of the building.

Build proof that fits on one page

A portfolio for an AI internship doesn't need to be big. It needs to be specific and easy to check. Project shapes that tend to read well:

For startups: a small deployed app on a model API that solves one narrow problem, with a short note on what broke and how you fixed it.
For labs and research groups: a careful reproduction of a published result, with honest notes on where your numbers differ.
For data and evaluation roles: an evaluation set of 100 to 200 examples for a real task, with a scoring method and a comparison of two models.
For non-technical roles: a teardown of an AI product's onboarding, or a workflow you automated for a student group with before-and-after time saved.

Python shows up most among specialized skills in US AI job postings, per the 2026 AI Index's analysis of Lightcast data, so it's a sensible default if you code. Add a README that explains the problem, approach and result in plain words, then put the link on your resume; our guide to writing a resume for an AI job shows how to frame it.

A realistic weekly routine during recruiting season

An illustrative plan for a student with a full course load, about six hours a week:

Monday (1 hour): check new postings. On the 1752vc careers board's AI jobs, set the level filter to Internship and sort by newest.
Tuesday and Wednesday (3 hours): improve one project or write it up.
Thursday (1 hour): send three tailored applications, each followed by a short note to a named person.
Friday (1 hour): email one research group or follow up once on anything older than ten days.

Over 12 weeks, that's 36 applications with notes and 12 research emails. If one in ten notes earns a conversation (illustrative; real rates vary), that's three or four conversations. A tracking sheet with columns for company, lane, role, date applied, contact, note sent, reply and next step keeps it honest.

Common mistakes

Listing courses instead of results. "Took Machine Learning" says less than "built a classifier that cut manual review time in half for our club."
Starting too late for big companies, too early for startups. Their calendars differ, so run two clocks.
Ignoring work authorization. F-1 students generally need authorization, such as school-approved Curricular Practical Training, before an off-campus internship. Ask your international student office early.

Where we land

In our view, the most reliable first AI internship for many students isn't the famous one. It's a campus research role or a startup that needs one thing built, followed by a bigger name the next summer. Proof compounds.

This week: pick a lane, build one small thing, and set a weekly check of the AI internships on the 1752vc careers board filtered to Internship. After graduation, see our guide on how to get a job in AI.

The bottom line

Interest in AI is everywhere on campus, so it doesn't separate anyone.

What separates you is a link that works.

Key takeaways

AI internships come from three lanes (big labs, AI startups, campus research groups), and each screens differently.
Per Handshake, the share of internship descriptions mentioning generative AI rose more than fourfold since 2023, yet competition stays tight.
Campus research and NSF-funded REU sites are realistic first steps; most REU summer deadlines fall between January and March.
One specific, public project tied to your lane often beats a long list of courses.
Per NACE's 2026 survey, 63.1 percent of 2024 to 2025 interns converted to full-time hires, so treating the internship as a long interview can pay off.

Frequently asked questions

Can a freshman get an AI internship?

It's possible, though large-company AI internships rarely take first-year students. A more realistic first step is a campus research role, a part-time project for a startup or a student group, or a program aimed at underclassmen. Use the year to finish core math and programming courses and build one public project, then aim for REU sites or startups the following summer.

Do AI internships require a PhD?

Many formal research internships at large labs are aimed at PhD students, but most AI internships are not. Applied machine learning, data and evaluation, and AI product or operations internships regularly hire undergraduates. Google's Student Researcher program is open to bachelor's students, and some research fellowships state that no PhD or prior publications are needed.

How do I get an AI research assistant position in college?

Read the faculty pages in your department, pick a few labs whose work you understand, and read one recent paper from each. Then email the professor or a PhD student in the lab with one specific question, your relevant courses, a project link and the hours you can commit. Ask whether the role would be paid, for credit or volunteer.

When should I apply for AI internships?

It depends on the employer. Large tech companies often open summer internship recruiting the previous fall, NSF notes most summer REU sites set deadlines between January and March, and startups and research groups tend to hire on a rolling basis closer to the start date. Running two calendars, one early and one late, covers most options.

Are AI internships paid?

Most internships at companies are paid, and NACE's 2026 compensation guide puts the average bachelor's-level intern wage at $23.35 an hour across fields, with technical roles often higher. Under federal wage rules, unpaid internships at for-profit companies have to pass the primary beneficiary test. Campus research roles vary: some pay, some offer credit, and some start as volunteer positions.

Sources

Disclaimer: This guide is for general education only and is not legal, tax or investment advice. Laws, market data and program terms change, so it may not reflect the latest developments or fit your situation. Treat it as a starting point, not a source of truth, and talk to a qualified lawyer, accountant or financial adviser before you make decisions.