2027 AI/ML internship jobs are being tracked daily

A GitHub repo tracks 2027 AI/ML internships and new grad roles daily.

speedyapply/2027-AI-College-Jobs has turned into a live job board for students chasing AI, machine learning, and data science roles. The repo says it updates daily and prioritizes postings from the last 120 days, with hundreds of openings already split across U.S. and international buckets.

At a glance, the numbers are hard to ignore: 194 U.S. internships, 215 U.S. new grad roles, 323 international internships, and 257 international new grad roles. That adds up to 989 listed opportunities in the README snapshot, which is a lot more useful than scrolling through disconnected company career pages one by one.

What this repo actually does

The project is simple on purpose: it collects AI/ML and data science jobs for college students who want internships or first jobs after graduation. The README also says the list is updated daily, and that it favors jobs posted within the last 120 days, which matters because stale job boards waste time fast.

The repo is organized around four main buckets: U.S. internships, U.S. new grad, international internships, and international new grad. That structure makes it easy to compare options without guessing whether a role is meant for a student, a recent graduate, or someone already deep into research.

There is also a separate pointer for software engineering applicants: speedyapply/2027-SWE-College-Jobs. That matters because a lot of students are applying across both tracks, and the split keeps the AI-focused list from getting buried under generalist openings.

• Updated daily
• Prioritizes jobs posted within 120 days
• Organized by region and career stage
• Includes FAANG+, Quant, and Other categories

Why students care about this list

For students, the real value is speed. A curated list cuts down the time spent hunting for roles at Google, Meta, Microsoft, and other employers that post on their own schedules.

The repo also surfaces salary data for some roles, which helps candidates sanity-check offers before they even apply. For example, the snapshot shows Meta research scientist internships at $50 per hour, TikTok applied scientist internships at $60 per hour, and NVIDIA research internships at $62 per hour.

“The best source of information is your own experiment.” — Andrew Ng

That quote fits this repo better than a generic job board slogan. Students can treat the list like a live experiment: apply early, track which companies respond, and compare which role types lead to interviews.

What the numbers say about the market

The salary spread in the README snapshot is wide enough to matter. Meta’s listed research intern roles sit at $50 per hour, Microsoft’s research intern role is listed at $52 per hour, TikTok’s applied scientist and AI software engineer intern roles are listed at $60 per hour, and Netflix’s AI/ML scientist intern role is listed at $63 per hour.

That spread tells a clear story: research-heavy AI internships, especially PhD-targeted ones, can pay more than many general software internships. It also shows that the same company can offer very different compensation depending on the team, location, and degree requirement.

• Meta: $50/hr
• Microsoft: $52/hr
• TikTok: $60/hr
• NVIDIA: $62/hr
• Netflix: $63/hr

There is another signal hiding in the role titles. Many of the highest-paying listings are tied to research, multimodal systems, reinforcement learning, or foundation models. That means students who can show real project depth in those areas may have a cleaner path into interviews than applicants with only coursework on the résumé.

For a broader view of hiring patterns in student recruiting, see our related OraCore.dev coverage of AI internship hiring patterns and new grad AI jobs.

What to do with this repo now

If you are applying this season, the practical move is to use the repo as a weekly pipeline, not a one-time browse. Check the newest entries first, sort by salary when it is listed, and keep a short list of companies that match your degree level and location.

The repo’s biggest strength is not just volume. It is the mix of recency, salary transparency, and role specificity, which makes it easier to spot openings that are actually worth the application effort. If you are a student aiming at AI research, applied ML, or data science, this list is probably more useful than a generic “top tech jobs” roundup.

My read: if the maintainers keep the daily update cadence, this repo will keep pulling in students who want a faster way to find real openings before they disappear. The question is whether applicants will use it like a living tracker, or treat it like another bookmark they never revisit.