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Data Science, AI & Machine Learning Internships

Explore 846 data science, AI, and machine learning internship opportunities for Summer 2026. This is the largest category with positions in data science, machine learning engineering, AI research, data engineering, and analytics.

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What’s Included

This category encompasses a wide range of data-focused roles:

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Why So Many Opportunities?

With 846 positions (nearly half of all internships), data science and AI roles have exploded due to:

• AI Revolution - ChatGPT and LLMs driving massive industry investment
• Data-Driven Decisions - Every company needs data scientists
• ML Infrastructure - Growing need for ML engineers and MLOps
• Specialized Domains - Healthcare AI, autonomous vehicles, robotics, fintech
• Research Labs - University and corporate research positions

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Example Roles by Specialization

Here are real examples of the types of data science internships available:

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Machine Learning Engineering

Building and deploying ML systems:

• ML Infrastructure Intern
• MLOps Engineer Intern
• Machine Learning Engineer Intern
• Computer Vision Engineer Intern
• NLP Engineer Intern

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AI Research

Cutting-edge research positions:

• AI Research Intern (often requires 🎓 advanced degree)
• Research Scientist Intern
• Applied Scientist Intern
• Deep Learning Research Intern

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Data Science

Analytical and modeling roles:

• Data Scientist Intern
• Quantitative Analyst
• Experimentation Scientist
• Product Data Scientist

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Data Engineering

Building data infrastructure:

• Data Engineer Intern
• Analytics Engineer Intern
• Data Platform Engineer
• Big Data Engineer Intern

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Business Intelligence & Analytics

Business-facing analytical roles:

• Data Analyst Intern
• Business Intelligence Intern
• Analytics Intern
• Marketing Analytics Intern

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Featured Companies with AI/ML Internships

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AI-First Companies

Companies building AI products:

• 🔥 OpenAI - Language models, ChatGPT
• Anthropic - AI safety and Claude
• DeepMind - AI research
• Scale AI - AI training data
• Hugging Face - Open-source AI tools

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Big Tech AI Labs

Major tech companies with AI divisions:

• 🔥 Google/Google Brain - AI research
• 🔥 Meta AI (FAIR) - AI research lab
• 🔥 Microsoft Research - AI systems
• 🔥 Amazon - Alexa AI, AWS AI
• 🔥 Apple - Machine Learning and AI

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Autonomous Vehicles

Self-driving technology:

• Waymo - Autonomous vehicles
• Cruise - Self-driving cars
• Tesla - Autopilot ML
• Aurora - Autonomous trucking

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Healthcare & Biotech AI

AI for medicine and biology:

• Ginkgo Bioworks - Synthetic biology
• Recursion Pharmaceuticals - Drug discovery AI
• Tempus - Healthcare AI
• Freenome - Cancer detection AI

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Fintech & Trading

Quantitative ML in finance:

• Quantitative trading firms
• Risk modeling
• Fraud detection ML
• Algorithmic trading

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Required Skills

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Technical Skills

Most data science internships expect: Programming:

• Python (NumPy, Pandas, Scikit-learn)
• SQL for data querying
• R (for some analytics roles)

Machine Learning:

• Supervised/unsupervised learning
• Deep learning frameworks (PyTorch, TensorFlow)
• Model evaluation and validation
• Feature engineering

Math & Statistics:

• Linear algebra
• Probability and statistics
• Calculus (for ML theory)
• Experimental design (A/B testing)

Data Tools:

• Jupyter notebooks
• Git version control
• Cloud platforms (AWS, GCP, Azure)
• Data visualization (Matplotlib, Tableau)

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Research Positions (🎓 Advanced Degree)

AI research internships often require or prefer:

• Master’s or PhD in progress
• Published research papers
• Strong mathematical background
• Experience with novel ML architectures

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How to Apply

Each listing in the main repository includes:

1. Direct application link to the company’s job posting
2. Simplify button for one-click autofill

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Save Time with Simplify

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Interview Process

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Common Interview Stages

1. Phone Screen - 30-minute recruiter call
2. Technical Screen - Coding + ML fundamentals
3. Take-Home Project - Real-world data science case study
4. On-site/Virtual - Multiple rounds:
   • Coding interview (data structures, algorithms)
   • ML theory (model selection, evaluation, tradeoffs)
   • Case study (solve a business problem with data)
   • Behavioral interview

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Typical DS/ML Interview Topics

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Application Tips

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Build Your Portfolio

1. Kaggle Competitions - Demonstrate ML skills with real datasets
2. GitHub Projects - Share your code and models
3. Research Papers - Publish at workshops or conferences
4. Blog Posts - Explain complex concepts or project walkthroughs
5. Coursework - Highlight relevant ML and stats classes

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Stand Out

• Domain Expertise - Combine ML with another field (biology, finance, etc.)
• Research Experience - Work with professors on ML projects
• Open Source - Contribute to ML libraries
• Internships - Previous data internships, even at smaller companies
• Competitions - Win or place highly in data science competitions

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Browse Related Categories

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Stay Updated

New data science and AI internships are added daily.

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Off-Season Data Science Opportunities

Looking for Fall, Winter, or Spring data internships?

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Resources for DS/ML Internships

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Learning Platforms

• Coursera: Andrew Ng’s ML courses, Deep Learning Specialization
• Fast.ai: Practical deep learning courses
• Kaggle Learn: Free micro-courses on ML topics
• Papers with Code: Latest research papers with implementations

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Communities

• r/MachineLearning: Reddit community for ML
• Towards Data Science: Medium publication
• ML Twitter: Follow researchers and practitioners
• Discord/Slack: Join ML learning communities

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Contributing

🙏 Found a data science or ML internship not on the list? Help the community by submitting it! When submitting, select “Data Science, AI & Machine Learning” as the category. See the contribution guidelines for details.