5 ways to use 2026 AI job lists

This GitHub list tracks 2026 AI/ML internships and new grad jobs updated daily.

If you are hunting AI roles for college or your first job, this list shows where to focus in one place. The repo has 5,364 stars and organizes openings by region, role type, and company tier.

1. Use it to find fresh openings fast

The biggest advantage is speed. The repository is updated daily and prioritizes jobs posted within the last 120 days, so you spend less time sorting stale listings and more time applying to roles that are still live.

That matters because internship and new grad roles can close quickly. If you check the list often, you can catch postings from companies like NVIDIA, Meta, and Microsoft while they are still within the active window.

• Update cadence: daily
• Freshness filter: last 120 days
• Best for: students who apply early and often

2. Compare internship and new grad paths

The repo splits roles into internships and new graduate jobs, which makes it easier to decide whether you should target summer experience or a full-time first role. That split also helps if you are applying across class years or want to keep both options open.

In the USA section alone, the list shows 190 internships and 223 new grad openings. Internationally, it shows 297 internships and 246 new grad roles, so you can compare the scale of each track before narrowing your search.

• USA internships: 190
• USA new grad roles: 223
• International internships: 297
• International new grad roles: 246

3. Filter by company tier and role type

The list groups jobs into FAANG+, Quant, and Other buckets, which is useful when you want to target specific employer types. That structure lets you move from broad browsing to a tighter shortlist without building your own spreadsheet first.

For example, you can scan research-heavy roles such as a TikTok data science intern, a Stripe PhD data scientist intern, or a Pinterest machine learning intern, then compare them against quant or general tech openings.

FAANG+ → Quant → Other Internship → New Grad USA → International

4. Target AI specialties that match your resume

This is not a generic job dump. Many roles are specific to AI subfields such as multimodal systems, reinforcement learning, recommendation, generative AI, and data science. That helps you match the listing to your coursework, research, or project history.

If your background fits model research, you can focus on PhD-oriented postings. If you are stronger in data work or applied ML, the list includes project and operations roles that still sit inside AI teams.

• Generative AI
• Multimodal AI
• Reinforcement learning
• Recommendation systems
• Data science and analytics

5. Use the repo as a job-search map

The README gives you direct paths into separate markdown files for each region and degree track, so the repo works like a map instead of a single page of links. That makes it easier to revisit the exact section you need without starting over.

It also points students to a related software engineering list, which is handy if you want to keep AI applications moving while widening your search. For anyone balancing internships, new grad roles, and backup plans, that structure saves time.

• USA internships and new grad pages
• International internships and new grad pages
• Linked SWE college jobs list for broader search

How to decide

If you want the fastest path to live AI openings, start with the daily-updated internship and new grad pages and sort by region first. If you already know your target area, use the FAANG+, Quant, and Other groupings to cut the list down to the employers you actually want.

Choose internship pages if you are still in school and want experience. Choose new grad pages if you are ready for full-time work. If you are somewhere in between, bookmark both and use the repo as a weekly application tracker.