speedyapply/2027-AI-College-Jobs: a daily-updated job list you read on GitHub

What the 2027 AI College Jobs list actually is

The README describes the repository as "a comprehensive list of AI/ML & Data Science jobs for college students in search of internships or new graduate positions." The badge at the top of the README reports 1871 jobs. That number is the whole product: a count, four tables, and a daily refresh.

The audience is narrow and stated plainly. College students, two tracks (internship and new graduate), two geographies (USA and international). The README splits the counts as 430 US internships, 447 US new grad roles, 533 international internships and 461 international new grad roles. If you are a mid-career engineer looking at AI roles, nothing here is aimed at you.

The repository has four top-level Markdown files besides the README: INTERN_INTL.md, NEW_GRAD_INTL.md, NEW_GRAD_USA.md and README.md. The README itself carries the US internship table. That is the entire structure. There is no build system, no schema, no API documented in the README, and no license file visible in the repository listing.

How the tables are organised, and why the tiering matters

Each table row is a job. The columns are Company, Position, Location, Salary, Posting and Age. Age is expressed in days, for example 0d, 5d, 14d, 20d. The README states that positions are updated daily and that jobs posted within the last 120 days are prioritised, so Age is effectively the freshness signal and 120 days is the outer bound.

Rows are grouped under three anchors in every table: FAANG+, Quant, and Other. The README links each group separately, so you can jump straight to Quant if that is what you want. The tiering is editorial, not derived from company data, and the README does not define which companies fall into FAANG+ versus Other. Treat the grouping as a convenience, not a ranking.

Salary appears as an hourly rate in the rows shown, for example $51/hr for a Rivian data engineering internship and $63/hr for a Netflix machine learning internship. The README gives no explanation of how salary is sourced, whether it is the posted range or a midpoint, or what happens when an employer posts no salary at all.

Reading the list without cloning it

The fastest path is the GitHub web view. The README is the US internship table, so opening the repository shows you 430 rows before you install anything. The other three tables are one click away in the file list.

If you prefer a terminal, cloning gives you all four files locally and lets you search them. The default branch is main, and the README gives the repository under the speedyapply organisation.

git clone https://github.com/speedyapply/2027-AI-College-Jobs.git

After the clone, the working directory holds README.md, INTERN_INTL.md, NEW_GRAD_INTL.md and NEW_GRAD_USA.md alongside .github. From there, a text search is the practical tool. Searching the README for a company name returns whole table lines, including the HTML anchor tags around the apply link. That is readable but not parseable without extra work.

To refresh, pull the same clone again. There is no command in the README that regenerates the tables yourself; the README only says the positions are updated daily, which implies the maintainers run the refresh, not you.

The apply links are third-party URLs, and the list cannot tell you if they are dead

Every Posting cell is an external link. In the rows shown, they point at Ashby, Microsoft's careers site, Adobe's Workday tenant, Netflix's Workday tenant, TikTok's lifeattiktok.com search pages and Meta's metacareers.com. The list is a directory of those URLs, nothing more.

The Age column tells you how long ago the row was added or last seen. It does not tell you whether the requisition is still open. A 0d row is fresh by the list's own clock, but the list has no documented mechanism for re-checking a URL after it is written. If a company closes a posting on day three, the row can still say 5d and still look live.

This is the single biggest practical limitation. The README does not document link validation, a status column, or a removal process. Your only defence is to open the link and read the employer page. Budget for that on every row you shortlist.

Compared with a general new-grad job board

A general board such as a university careers portal or a large aggregator indexes everything: finance, consulting, mechanical engineering, marketing. Its search is built for breadth, and its freshness varies per employer because each employer controls its own posting.

The difference here is the filter and the cadence. The README states the list is AI/ML and Data Science only, and that it prioritises postings from the last 120 days. That combination is hard to reproduce on a general board without saved searches and manual date filtering. The trade-off is scope: if your search widens to backend engineering or data analyst roles outside AI, you need the sibling list the README points at, speedyapply/2027-SWE-College-Jobs, or a different source entirely.

The other difference is the salary column. General boards often omit pay. Here it is a first-class column, which is useful for comparing offers early, but the README does not say how the figure is derived, so treat it as a lead rather than a verified number.

Maintenance, licensing and what the repository does not state

The last push to the default branch was on 2026-09-09, eight days before this was written, which is consistent with the README's claim of a daily update. The repository is not archived. There are no releases, which fits a project that ships content rather than code.

There is no license file in the top-level listing. That matters if you intend to reuse the table data: without a stated license, the default position is that the maintainers retain rights, and you should not assume you can republish the compiled list. The individual job postings are not the maintainers' content in any case; they belong to the employers, and each apply link leads to that employer's own terms.

The README does not document a contribution process, a data source, a scraper, or a rollback path if a bad row lands. The .github directory exists but its contents are not described in the README. If you need to know how a row got there, the README is silent.

Editorial conclusion

Adopt it if you are a student hunting 2027 AI, ML or data internships and you want salary and posting age visible in the same row as the apply link. Skip it if you need structured data for a script, or if you are applying outside the US and international tracks. Before relying on it, check the posting age column on the row you care about and open the employer link, because the list only reports what it last scraped and the README documents no verification step beyond that.

Frequently asked questions

Official sources

1. Issues
2. Project website
3. README
4. speedyapply/2027-AI-College-Jobs on GitHub