How to Land a Machine Learning Internship: Your 2023 Guide

Written by Coursera • Updated on

Gain work experience, expand your professional network, and set yourself up for success with a machine learning internship. Learn more about what they are and how to land one.

[Featured image] A machine language intern wearing glasses and a grey hoodie sits in front of a laptop computer.

Machine learning lies at the heart of some of the most impactful goods and services we use every day, from the search engines we rely on for school and work to the video streaming platforms we relax with at night. And, as machine learning models become ever more important to businesses around the world, so too do professionals capable of training, designing, and deploying them. If you’re currently working toward a career in machine learning, then an internship can be a vital step in gaining the real world work experience that you’ll need to succeed in the field. But, what can you expect from a machine learning internship and how can you actually land one? 

In this article, you’ll learn all you need to know about machine learning internships, including their benefits, duties, and the skills you’ll likely use on the job. You’ll also learn how to find machine learning internships online, some of the most common requirements for applicants, and explore tips on how to put your best foot forward when applying. 

Machine learning interns: benefits, duties, and required skills 

Working in a machine learning internship can provide you with a lot of professional and personal benefits. Here’s what you need to know about the benefits of an internship, what you’ll do, and the skills you’ll need: 


There are several benefits to an internship. In addition to having something to add to your resume, you’ll gain real-world work experience, professional networking opportunities, and the chance to hone important workplace skills like collaboration and communication. 

Internships supplement the training you have received in school with real-world work experience that gives you a taste of what your professional future may look like. 


The duties and responsibilities you’ll perform as a machine learning intern will vary from one internship to another. Nonetheless, some general tasks you may perform include the following: 

  • Support development or engineering teams to complete projects


Whether you’re an aspiring data scientist or an AI engineer hoping to one day work on computer vision, the skills you’ll hone in a machine learning internship can help set you up for future professional success. 

Here are just some of the technical and people skills you’ll likely need to use in your internship: 

Technical skills 

At their core, ML internships center around a strong technical skill set that requires you to be comfortable with everything from training ML algorithms to using advanced analytics techniques for business intelligence purposes. 

While every internship will emphasize skills differently, here are some common ones you can expect to encounter when searching for internships yourself: 

  • Knowledge of programming languages such as R, Python, Java, or C/C++ 

  • Experience building models with deep learning frameworks like Tensorflow 

  • Knowledge of relevant statistical, mathematical, and computational concepts

People skills 

During your internship, you’ll not only need to use a range of technical but also people skills to ensure you do the best possible work. Some of the most common interpersonal skills you’ll likely use during your internship include: 

  • Ability to collaborate with others and work on a team

  • Strong written and verbal communication skills

  • Curious disposition that allows you to think both critically and outside the box

Applications of Machine Learning

Machine learning internships: Where to find them, requirements, and tips on applying 

An internship can be a big step in your professional journey, opening doors to new opportunities. Here’s how you can find them, their most common requirements, and some tips on successfully applying to them:  

How to find a machine learning internship 

There are two primary ways to find an internship online: by searching on a dedicated job posting site, such as Indeed or LinkedIn, or by searching for them on a company’s dedicated job page. Here’s how you can do both: 

Job posting sites

To find a machine learning internship on a job posting site, go to your site of choice and simply search for “machine learning internship.” From there you can view a list of currently available internships.

Some popular websites to search for internships include: 

Business’ dedicated job page

To find an internship on a business’ dedicated job page, go to a search engine, such as Google, and simply search “[business’ name] jobs.” Once on the dedicated job page, search it for “machine learning internship” and see if any are available. You might also consider searching for a related position, such as “machine learning engineer internship” or “data science internship.”

Machine learning internship requirements 

The requirements for a machine learning internship vary greatly from one to another. 

While some require that you be enrolled in a master’s or doctoral program, others might only require that you be in a bachelor’s program. Typically, you can expect that internships will require you to be pursuing a degree in a field related to machine learning, such as computer science or data science. 

All internships will likely require that you have some understanding of programming languages like Python or R, working with ML algorithms and training ML models, and are comfortable working with various types of data

Tips on applying for internships

It’s always important to put your best foot forward when applying for an internship. To help you do just that, here are some key things you should keep in mind throughout the process:

1. Apply ahead of time. Many internships start looking for candidates well before the start date, so make sure to apply well in advance for the internship. Many summer internships, for example, start looking for applicants as early as spring or early winter. 

2. Apply to multiple internships. Machine learning internships can be highly competitive, so it’s wise to apply to many different ones to increase your chances of being accepted.

3. Use a portfolio. One of the best ways to highlight your skills is through a portfolio that showcases related machine learning projects you’ve done in the past.

4. Use your personal network to find opportunities. One of the most valuable resources for finding a suitable internship is your existing network of peers, mentors, and professional contacts. 

5. Ask for recommendation letters early. Some internships may require that you provide a letter of recommendation when applying, so it’s important to source those recommendations early enough in the process to meet the application deadline. 

6. Tailor your resume and cover letter. Make sure to tailor both your resume and cover letter to each internship and to proofread all your materials before submitting them. 

Read more: How to Write a Cover Letter and Get Noticed

Prepare for your internship 

Internships can be a stepping stone to a future career as a machine learning professional. Get ready for your next internship by taking a cost-effective, online course through Coursera. 

A description of Andrew Ng’s Machine Learning Specialization states that the specialization provides a broad introduction to modern machine learning, including supervised learning, unsupervised learning, and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. 



Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng


(7,668 ratings)

128,073 already enrolled


Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection


Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Big savings for your big goals! Save $200 on Coursera Plus.

  • For a limited time, save like never before on a new Coursera Plus annual subscription (original price: $399 | after discount: $199 for one year).
  • Get unlimited access to 7,000+ courses from world-class universities and companies—for less than $20/month!
  • Gain the skills you need to succeed, anytime you need them—whether you’re starting your first job, switching to a new career, or advancing in your current role.