Students & New Grads
Internships, research, competitions, campus recruiting timelines and grad-school paths for breaking into AI.
AI jobs for new grads are won earlier than most students think. This page maps the whole student runway: machine learning internship recruiting, a month-by-month campus recruiting timeline, research opportunities, competitions, grad-school decisions and student-specific resources — each with concrete tasks per stage rather than generic advice. Start with the key takeaways, then work the timeline tab by tab.
The key takeaways panel distills what the data says matters most, then each tab goes deep: what to do each semester, when internship applications open, how to get research experience without connections, and which competitions actually register with hiring managers. Pair this with the education ROI comparison if you are weighing grad school against going straight in.
Explore: Become an ML engineer · Education ROI · Job search toolkit
Key takeaways
- ›Start early: Sophomore year for internships, Junior year for top companies
- ›Research matters: Publications boost grad school and research roles
- ›Competitions = portfolio: Kaggle medals impress employers
- ›Campus recruiting: Apply July-October for best chances
- ›Multiple pathways: Internship → FT, Research → Grad school, or both
AI/ML Internship Guide
How to land AI internships at top companies
- ›Master Python and data structures
- ›Take intro ML course (Andrew Ng)
- ›Start 1-2 personal projects
- ›Join AI/CS clubs
- ›Attend hackathons
- ›Complete ML/DL courses
- ›Build 3-4 portfolio projects
- ›Contribute to open source
- ›Apply for REU programs
- ›Start LeetCode practice
- ›Apply to FAANG + AI companies
- ›Kaggle competitions
- ›Research with professor (if grad school bound)
- ›Interview prep (AlgoExpert, Pramp)
- ›Build advanced projects (end-to-end ML systems)
- ›Full-time job applications (start July-August)
- ›Return offer from previous internship
- ›Graduate school applications (if applicable)
- ›Capstone/thesis projects
- ›Interview throughout fall
Top Programs
Requirements
- ›CS/ML coursework
- ›Strong coding skills
- ›Previous projects/internships preferred
Requirements
- ›Strong ML background
- ›Research experience preferred
- ›Publication track record (for research interns)
Requirements
- ›PhD student or exceptional undergrad
- ›Strong research background
- ›Publications preferred
Focus Areas
- ›Computer Vision
- ›Autonomous Driving
- ›GPU Computing
Requirements
- ›Computer vision experience
- ›C++ and Python
- ›Passion for autonomous driving
Application Tips
- ›Apply early (September-October for summer)
- ›Tailor resume to each company
- ›Build projects relevant to company's work
- ›Network with employees on LinkedIn
- ›Attend company info sessions
- ›Practice coding interviews (LeetCode, HackerRank)
- ›Prepare ML system design (for senior students)