If you want to get into AI or machine learning, you’ve likely noticed that just finishing courses isn’t enough anymore. Recruiters look for proof that you can handle real-world problems, work with messy data, and actually build and deliver models. Internships are the best way to show these skills. In this article, I’ll share the top AI/ML internships you can apply for in 2026.
Top Internships for AI/ML Freshers
Let’s look at some of the most popular AI/ML internships around the world and what you’ll do if you get one.
Research Scientist Intern at Adobe 🇮🇳
Adobe’s research internships in India focus more on applied research than on product engineering. Instead of just building models, you’ll work on problems like document intelligence, computer vision, generative AI, and creative tools.
This role is interesting because it combines academic research with real-world applications. You might create a new model, but it will usually connect to something Adobe can use or test in-house.
In practice, candidates who stand out here typically have:
- Strong fundamentals in machine learning theory (optimization, probability, deep learning)
- Hands-on work with PyTorch or TensorFlow
- Exposure to papers + implementation, not just Kaggle-style workflows
Apply for this internship at Adobe here.
LLM Research Intern at Sony 🇮🇳
Sony’s AI teams have been steadily working on LLMs, multimodal AI, and audio-visual intelligence, especially in their India offices.
This internship focuses on modern generative AI workflows. Instead of working with traditional models, you’ll spend more time with large pre-trained systems.
Strong candidates here usually demonstrate:
- Experience with transformers (Hugging Face ecosystem)
- Projects involving RAG pipelines or LLM apps
- Understanding of tokenization, embeddings, and context windows
Apply for this internship at Sony here.
AI/ML Intern at Adobe 🇺🇸
This internship is more focused on products than research. You’ll probably work on feature engineering pipelines, model deployment, and scaling ML systems for real users.
Adobe in the US values people who can handle the whole ML process, not just building models.
Candidates who stand out here often have:
- Projects deployed using Flask, FastAPI, or cloud platforms
- Experience with ML pipelines (Airflow, MLflow, etc.)
- Clean, readable GitHub repositories
Apply for this internship at Adobe here.
ML Engineer Intern at Tesla 🇺🇸
Tesla’s internships focus on real-world, high-impact machine learning systems.
You won’t be building simple models here. Instead, you’ll work on computer vision for self-driving cars, sensor fusion systems, and real-time inference pipelines.
This environment rewards:
- Strong coding skills (especially in Python and C++)
- Deep understanding of data pipelines and performance optimization
- Comfort with large-scale datasets
Apply for this internship at Tesla here.
ML Engineer Intern at Snap Inc. 🇬🇧
Snap’s ML projects are closely connected to user experiences, such as recommendations, AR filters, and content personalization.
This internship combines machine learning, product testing, and real-time systems.
Strong candidates demonstrate:
- Understanding of metrics (CTR, retention, engagement)
- Experience with recommendation systems or ranking models
- Ability to think in terms of user impact, not just model accuracy
Apply for this internship at Snap Inc. here.
ML Engineer Intern at Perplexity 🇬🇧
Perplexity is part of a new group of AI-focused companies that build directly on top of LLMs.
As an intern here, you’ll work on search and LLM hybrid systems, retrieval-augmented generation (RAG), and improving how queries are understood and ranked.
What stands out in candidates:
- Experience building LLM-powered applications
- Understanding of vector databases and embeddings
- Ability to evaluate model outputs critically (not just generate them)
Apply for this internship at Perplexity here.
Closing Thoughts
AI/ML internships are now less about learning and more about helping companies find future hires early.
Companies use internships to find people who can handle uncertainty, work with real data, and go beyond just following tutorials. If you prepare as if you’re already on a team, like building, testing, and improving your work, you’ll fit what these roles are looking for.
I hope you enjoyed the article! Follow me on Instagram for more AI and machine learning tips. You can also check out my book, Hands-On GenAI, LLMs & AI Agents, to get career-ready in AI.
